Gene expression analysis techniques using gene ranking and statistical models for identifying biological sample characteristics

ABSTRACT

Techniques for determining one or more characteristics of a biological sample using rankings of gene expression levels in expression data obtained using one or more sequencing platforms is described. The techniques may include obtaining expression data for a biological sample of a subject. The techniques further include ranking genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking and determining using the gene ranking and a statistical model, one or more characteristics of the biological sample.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 and is a continuation of U.S. patent application Ser. No. 17/113,008, filed Dec. 5, 2020, titled “GENE EXPRESSION ANALYSIS TECHNIQUES USING GENE RANKINGS AND STATISTICAL MODELS FOR IDENTIFYING BIOLOGICAL SAMPLE CHARACTERISTICS,” which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/943,976, filed Dec. 5, 2019, titled “MACHINE LEARNING TECHNIQUES FOR GENE EXPRESSION ANALYSIS” and U.S. Provisional Patent Application Ser. No. 63/060,512, filed Aug. 3, 2020, titled “MACHINE LEARNING TECHNIQUES FOR DETERMINING PERIPHERAL T-CELL LYMPHOMA (PTCL) SUBTYPE USING GENE EXPRESSION DATA”, the entire contents of each of which are incorporated by reference herein.

FIELD

Aspects of the technology described herein relate to determining characteristics of a biological sample obtained from a subject known to have, suspected of having, or at risk of having cancer by sequencing the biological sample using one or multiple sequencing platforms and analyzing the resulting gene expression data using machine learning techniques. In particular, the technology described herein involves using gene expression data from one or multiple sequencing platforms to determine characteristics of the biological sample, such as tissue of origin and cancer grade.

BACKGROUND

Characteristics of a biological cell may relate to the expression levels of certain genes. For example, a cancerous cell may have some genes upregulated and other genes downregulated relative to a normal, healthy cell. This relationship between cell characteristics and gene expression levels may be utilized in analyzing gene expression data for biological cells, such as data obtained using a gene expression microarray or by performing next generation sequencing, to determine characteristics of the biological cells.

SUMMARY

Some embodiments are directed to a computer-implemented method, comprising using at least one computer hardware processor to perform: obtaining expression data obtained at least in part by sequencing a biological sample of a subject having, suspected of having or at risk of having cancer, the expression data comprising expression levels for a plurality of genes, the plurality of genes comprising a set of genes; ranking at least some genes in the set of genes, based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of gene rankings of at least some of the genes in the set of genes obtained, at least one characteristic of the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

The at least one characteristic may be selected from cancer grade for cells in the biological sample (e.g., breast cancer grade, kidney clear cell cancer grade, lung adenocarcinoma grade), tissue of origin for cells in the biological sample (e.g., lung, pancreas, stomach, colon, liver, bladder, kidney, thyroid, lymph nodes, adrenal gland, skin, breast, ovary, prostrate, or cell of origin in a tissue such as e.g. germinal center B-cell (GCB) or activated B-cell (ABC)), histological information (tissue type, such as e.g. adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma) for cells in the biological sample, and cancer subtype (e.g. PTCL subtype such as, anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL)), viral status (e.g., HPV status, such as HPV-positive or HPV-negative for head and neck squamous cell carcinoma) for cells in the biological sample.

In some embodiments, the at least one characteristic of the biological sample is a physiological characteristic of cells in the biological sample or a tissue from which the cells originate. In some embodiments, the at least one characteristic is selected from cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample.

In some embodiments, the method further comprises performing sequencing of the biological sample using a gene expression microarray prior to obtaining the expression data. In some embodiments, the method further comprises performing next generation sequencing of the biological sample prior to obtaining the expression data.

In some embodiments, the at least one characteristic includes cancer grade for cells in the biological sample. In some embodiments, the at least one characteristic includes tissue of origin for cells in the biological sample.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. In some embodiments, the set of genes is selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 3 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 5 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 20 genes selected from the group of genes listed in Table 1.

In some embodiments, the subject has, is suspected of having, or is at risk of having kidney cancer. In some embodiments, the subject has, is suspected of having, or is at risk of having clear cell kidney cancer. In some embodiments, the set of genes is selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 3 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 5 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 20 genes selected from the group of genes listed in Table 2.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. In some embodiments, the set of genes is selected from the group of genes listed in Table 3. In some embodiments, the set of genes comprises at least 3 genes selected from the group of genes listed in Table 3. In some embodiments, the set of genes comprises at least 5 genes selected from the group of genes listed in Table 3. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 3. In some embodiments, the set of genes comprises at least 20 genes selected from the group of genes listed in Table 3.

In some embodiments, the subject has, is suspected of having, or is at risk of having head and neck squamous cell carcinoma. In some embodiments, the set of genes is selected from the group of genes listed in Table 6. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 6.

In some embodiments, the at least one characteristic includes human papillomavirus status for cells in a biological sample. In some embodiments, the set of genes is selected from the group of genes listed in Table 8. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 8.

In some embodiments, the method further comprises ranking at least some genes in a second set of genes based on their expression levels in the expression data to obtain a second gene ranking; and determining, using the second gene ranking and a second statistical model trained using second training data indicating a plurality of rankings for the at least some of the genes in the second set of genes, at least one second characteristic of the biological sample.

In some embodiments, the at least one second characteristic includes cancer grade for cells in the biological sample. In some embodiments, the at least one second characteristic includes tissue of origin for cells in the biological sample.

In some embodiments, determining the gene ranking comprises determining a relative rank for each gene in the set of genes based on the expression levels. In some embodiments, determining the at least one characteristic further comprises providing the gene ranking as input to the statistical model and obtaining an output indicating the at least one characteristic. In some embodiments, the statistical model comprises a gradient boosted decision tree classifier. In some embodiments, the statistical model comprises a classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

In some embodiments, the set of genes includes at least 5 genes. In some embodiments, the set of genes consists of 5-50 genes. In some embodiments, the set of genes consists of 5-300 genes.

In some embodiments, the method further comprises presenting, to a user, an indication of the at least one characteristic. In some embodiments, presenting the indication of the at least one characteristic further comprises displaying the at least one characteristic to the user in a graphical user interface (GUI).

In some embodiments, the at least one characteristic includes cancer grade for cells in the biological sample, and the cancer grade is selected from the group consisting of Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5. In some embodiments, the at least one characteristic includes tissue of origin for cells in the biological sample, and the tissue of origin is selected from the group consisting of lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue. In some embodiments, the at least one characteristic includes tissue type for cells in the biological sample, and the tissue type is selected from the group consisting of adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma.

In some embodiments, the at least one characteristic includes human papillomavirus (HPV) status for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table 8. In some embodiments, the at least one characteristic includes a subtype of peripheral T-cell lymphoma (PTCL) for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table 10. In some embodiments, the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL).

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 1. In some embodiments, the subject has, is suspected of having, or is at risk of having kidney cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 2. In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 3. In some embodiments, the subject has, is suspected of having, or is at risk of having Diffuse Large B-Cell Lymphoma (DLBCL), the set of genes comprises at least 10 genes selected from the group of genes listed in Table 3, and the at least one characteristic is a cell of origin selected from the group consisting of germinal center B-cell (GCB) and activated B-cell (ABC). In some embodiments, the subject has, is suspected of having, or is at risk of having lung adenocarcinoma, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 6.

In some embodiments, the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample.

In some embodiments, determining the at least one characteristic further comprises providing the gene ranking as an input to the statistical model and obtaining an output indicating the at least one characteristic. In some embodiments, the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample.

In some embodiments, the subject has, is suspected of having, or is at risk of having head and neck squamous cell carcinoma, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 8. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 8.

In some embodiments, the at least one characteristic includes human papillomavirus (HPV) status for cells in a biological sample. In some embodiments, the at least one characteristic includes a subtype of peripheral T-cell lymphoma (PTCL) for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table 10. In some embodiments, the set of genes comprises at least 10 genes selected from the groups of genes listed in Table 10. In some embodiments, the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL).

Some embodiments are directed to a system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform a method. The method comprises obtaining expression data obtained at least in part by sequencing a biological sample of a subject having, suspected of having or at risk of having cancer, the expression data comprising expression levels for a plurality of genes, the plurality of genes comprising a set of genes; ranking at least some genes in the set of genes, based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of gene rankings of at least some of the genes in the set of genes obtained, at least one characteristic of the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

Some embodiments are directed to at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining expression data obtained at least in part by sequencing a biological sample of a subject having, suspected of having or at risk of having cancer, the expression data comprising expression levels for a plurality of genes, the plurality of genes comprising a set of genes; ranking at least some genes in the set of genes, based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of gene rankings of at least some of the genes in the set of genes obtained, at least one characteristic of the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

Some embodiments are directed to a method, comprising using at least one computer hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model trained using training data indicating a plurality of rankings for at least some genes in the at least one set of genes, tissue of origin for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the at least one set of genes.

In some embodiments, the expression data was obtained using a gene expression microarray. In some embodiments, the expression data was obtained by performing next generation sequencing. In some embodiments, the tissue of origin is selected from the group consisting of lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue.

In some embodiments, determining, using the at least one gene ranking and the at least one statistical model, tissue type for at least some of the cells in the biological sample. In some embodiments, the tissue type is selected from the group consisting of adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma. In some embodiments, a combination of the tissue of origin and the tissue type is selected from the group consisting of lung adenocarcinoma, lung squamous cell carcinoma, melanoma, breast carcinoma, colorectal adenocarcinoma, ovarian serous cystadenocarcinoma, phenochromocytoma, bladder urothelial carcinoma, cervical squamous cell carcinoma, glioblastoma multiforme, head squamous cell carcinoma, neck squamous cell carcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, paraganglioma, prostate adenocarcinoma, sarcoma, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. In some embodiments, the subject has, is suspected of having, or is at risk of having Diffuse Large B-Cell Lymphoma (DLBCL). In some embodiments, the tissue of origin is a cell of origin selected from the group consisting of germinal center B-cell (GCB) and activated B-cell (ABC). In some embodiments, a set of genes of the at least one set of genes is selected from the group of genes listed in Table 3. In some embodiments, a set of genes of the at least one set of genes comprises at least 3 genes selected from the group of genes listed in Table 3. In some embodiments, a set of genes of the at least one set of genes comprises at least 5 genes selected from the group of genes listed in Table 3. In some embodiments, a set of genes of the at least one set of genes comprises at least 10 genes selected from the group of genes listed in Table 3.

In some embodiments, a set of genes of the at least one set of genes includes at least 5 genes. In some embodiments, a set of genes of the at least one set of genes consists of 5-100 genes. In some embodiments, a set of genes of the at least one set of genes consists of 10-200 genes. In some embodiments, a set of genes of the at least one set of genes consists of 20-100 genes. In some embodiments, a set of genes of the at least one set of genes consists of 50-100 genes.

In some embodiments, the expression data includes values, each representing an expression level for a gene in the at least one set of genes, and determining a gene ranking of the at least one gene ranking comprises determining a relative rank for each gene in one of the at least one set of genes based on the values. In some embodiments, determining the at least one characteristic further comprises using the at least one gene ranking as an input to the at least one statistical model and obtaining an output indicating the tissue of origin.

In some embodiments, the at least one statistical model comprises a gradient boosted decision tree classifier. In some embodiments, the at least one statistical model comprises at least one classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

In some embodiments, the at least one set of genes comprises a first set of genes associated with predicting a first type of tissue and a second set of genes associated with predicting a second type of tissue.

Some embodiments are directed to a system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform a method. The method comprises obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model trained using training data indicating a plurality of rankings for at least some genes in the at least one set of genes, tissue of origin for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the at least one set of genes.

Some embodiments are directed to at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model trained using training data indicating a plurality of rankings for at least some genes in the at least one set of genes, tissue of origin for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the at least one set of genes.

Some embodiments are directed to a method, comprising using at least one computer hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of rankings for at least some genes in the set of genes, cancer grade for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

In some embodiments, the expression data was obtained using a gene expression microarray. In some embodiments, the expression data was obtained by performing next generation sequencing. In some embodiments, the cancer grade is selected from the group consisting of at least Grade 1, Grade 2, and Grade 3. In some embodiments, the cancer grade is selected from the group consisting of at least Grade 1, Grade 2, Grade 3, and Grade 4. In some embodiments, the cancer grade is selected from the group consisting of Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. In some embodiments, the set of genes is selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 3 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 5 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 1.

In some embodiments, the subject has, is suspected of having, or is at risk of having kidney cancer. In some embodiments, the subject has, is suspected of having, or is at risk of having clear cell kidney cancer. In some embodiments, the set of genes is selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 3 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 5 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 2.

In some embodiments, the subject has, is suspected of having, or is at risk of having lung adenocarcinoma. In some embodiments, the set of genes is selected from the group of genes listed in Table 6. In some embodiments, the set of genes comprises at least 10 genes selected from the group of genes listed in Table 6. In some embodiments, the set of genes includes at least 50 genes. In some embodiments, the set of genes consists of 10-100 genes. In some embodiments, the set of genes consists of 10-30 genes.

In some embodiments, the expression data includes values, each representing an expression level for a gene in the set of genes, and determining the gene ranking comprises determining a relative rank for each gene in the set of genes based on the values. In some embodiments, determining that at least one characteristic further comprises using the gene ranking as an input to the statistical model and obtaining an output indicating the cancer grade.

In some embodiments, the statistical model comprises a gradient boosted decision tree classifier. In some embodiments, the statistical model comprises a classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

Some embodiments are directed to a system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform a method. The method comprises obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of rankings for at least some genes in the set of genes, cancer grade for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

Some embodiments are directed to at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking; and determining, using the gene ranking and a statistical model trained using training data indicating a plurality of rankings for at least some genes in the set of genes, cancer grade for at least some of the cells in the biological sample, wherein each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the set of genes.

Some embodiments are directed to a method, comprising using at least one computer hardware processor to perform using at least one computer hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model, a subtype of peripheral T-cell lymphoma (PTCL) for at least some of the cells in the biological sample.

In some embodiments, the at least one statistical model was trained using training data indicating a plurality of rankings of expression levels for at least some genes in the at least one set of genes. In some embodiments, each of the plurality of gene rankings is obtained based on respective expression levels for the at least some genes in the at least one set of genes.

In some embodiments, the expression data was obtained using a gene expression microarray. In some embodiments, the expression data was obtained by performing next generation sequencing. In some embodiments, the expression data was obtained using a hybridization-based expression assay.

In some embodiments, the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL). In some embodiments, the subtype of PTCL is selected from the group consisting of: Peripheral T-Cell Lymphoma, Not Otherwise Specified (PTCL-NOS), anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), Natural killer/T-cell lymphoma (NKTCL), Sezary syndrome, adult T-cell leukemia/lymphoma (ATLL), enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, hepatosplenic gamma-delta T-cell lymphoma, T-cell lymphomas of Follicular T-cell (TFH) origin, T-cell lymphomas of the gastrointestinal tract, and cutaneous T-cell lymphomas.

In some embodiments, a set of genes of the at least one set of genes is selected from the group of genes listed in Table 10. In some embodiments, a set of genes of the at least one set of genes comprises at least 3 genes selected from the group of genes listed in Table 10. In some embodiments, a set of genes of the at least one set of genes comprises at least 5 genes selected from the group of genes listed in Table 10. In some embodiments, a set of genes of the at least one set of genes comprises at least 10 genes selected from the group of genes listed in Table 10. In some embodiments, a set of genes of the at least one set of genes comprises at least 50 genes selected from the group of genes listed in Table 10.

In some embodiments, a set of genes of the at least one set of genes includes at least one up-regulated in AITL gene. In some embodiments, a set of genes of the at least one set of genes includes at least one down-regulated in AITL gene. In some embodiments, a set of genes of the at least one set of genes includes at least one MF profile gene.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. In some embodiments, the subject has, is suspected of having, or is at risk of having peripheral T-cell lymphoma (PTCL).

In some embodiments, a set of genes of the at least one set of genes includes at least 5 genes. In some embodiments, a set of genes of the at least one set of genes consists of 5-100 genes. In some embodiments, a set of genes of the at least one set of genes consists of 10-200 genes. In some embodiments, a set of genes of the at least one set of genes consists of 20-100 genes. In some embodiments, a set of genes of the at least one set of genes consists of 50-100 genes.

In some embodiments, the expression data includes values, each representing an expression level for a gene in the at least one set of genes, and determining a gene ranking of the at least one gene ranking comprises determining a relative rank for each gene in one of the at least one set of genes based on the values.

In some embodiments, determining the subtype of PTCL further comprises using the at least one gene ranking as an input to the at least one statistical model and obtaining an output indicating the subtype of PTCL.

In some embodiments, the at least one statistical model comprises a gradient boosted decision tree classifier. In some embodiments, the at least one statistical model comprises at least one classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

In some embodiments, the at least one statistical model includes a multi-class classifier. In some embodiments, the multi-class classifier has at least four outputs each corresponding to a different subtype of PTCL. In some embodiments, the at least four outputs include a first output corresponding to anaplastic large cell lymphoma (ALCL), a second output corresponding to angioimmunoblastic T-cell lymphoma (AITL), a third output corresponding to natural killer/T-cell lymphoma (NKTCL), and a fourth output corresponding to adult T-cell leukemia/lymphoma (ATLL).

In some embodiments, the at least one statistical model comprises a plurality of classifiers corresponding to different subtypes of PTCL. In some embodiments, the plurality of classifiers includes a first classifier, a second classifier, a third classifier, and a fourth classifier, wherein the first classifier corresponds anaplastic large cell lymphoma (ALCL), a second classifier corresponds to angioimmunoblastic T-cell lymphoma (AITL), a third classifier corresponds to natural killer/T-cell lymphoma (NKTCL), and a fourth classifier corresponds to adult T-cell leukemia/lymphoma (ATLL). In some embodiments, the at least one set of genes includes a first set of genes associated with a first classifier of the plurality of classifiers and a second set of genes associated with a second classifier of the plurality of classifiers.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. In some embodiments, the subject has, is suspected of having, or is at risk of having PTCL.

In some embodiments, the method further comprises presenting, to a user, an indication of the subtype of PTCL. In some embodiments, presenting the indication of the subtype of PTCL further comprises displaying the subtype of PTCL to the user in a graphical user interface (GUI).

Some embodiments are directed to a system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform a method. The method comprises obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model, a subtype of peripheral T-cell lymphoma (PTCL) for at least some of the cells in the biological sample.

Some embodiments are directed to at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining expression data for cells in a biological sample of a subject having, suspected of having, or at risk of having cancer; ranking at least some genes in at least one set of genes based on their expression levels in the expression data to obtain at least one gene ranking; and determining, using the at least one gene ranking and at least one statistical model, a subtype of peripheral T-cell lymphoma (PTCL) for at least some of the cells in the biological sample.

BRIEF DESCRIPTION OF DRAWINGS

Various aspects and embodiments will be described with reference to the following figures. The figures are not necessarily drawn to scale.

FIG. 1 is a diagram of an illustrative process for determining one or more characteristics of a biological sample based on one or more respective gene rankings for the biological sample using the machine learning techniques described herein.

FIG. 2 is a diagram of an illustrative process for determining a characteristic of a biological sample based on using multiple statistical models to obtain multiple characteristic predictions and aggregating the characteristic predictions using the machine learning techniques described herein.

FIG. 3 is a flow chart of an illustrative process for determining a characteristic of a biological sample using a gene ranking and a statistical model, using the machine learning techniques described herein.

FIG. 4 is a flow chart of an illustrative process for determining tissue of origin for cells in a biological sample using the machine learning techniques described herein.

FIG. 5 is a flow chart of an illustrative process for determining cancer grade for cells in a biological sample using the machine learning techniques described herein.

FIG. 6A shows example different data sets, associated clinical cancer grade for samples of the data sets, and predicted cancer grade obtained using the machine learning techniques described herein, for determining breast cancer grade.

FIG. 6B shows example the enrichment signatures for different pathways, illustrating gene expression profiles associated with breast cancer Grade 1 and Grade 3.

FIG. 6C shows example different data sets, associated clinical cancer grade for samples of the data sets, and predicted cancer grade, using the machine learning techniques described herein, for determining breast cancer grade.

FIG. 6D shows example the enrichment signatures for different pathways, illustrating gene expression profiles associated with breast cancer Grade 1 and Grade 3.

FIG. 7 is an illustrative plot of true positive rate versus false positive rate for predicting breast cancer grade of different biological samples using the machine learning techniques described herein.

FIG. 8A is a flowchart of an illustrative process for selecting a gene set, using the machine learning techniques described herein.

FIG. 8B is a flowchart of an illustrative process for selecting a gene set, using the machine learning techniques described herein.

FIG. 9A is an exemplary plot of quality score versus number of genes used for determining tissue of origin, using the machine learning techniques described herein.

FIG. 9B is an exemplary plot of F1 score versus number of genes used for determining tissue of origin for Diffuse Large B-Cell Lymphoma (DLBCL), such as germinal center B-cell (GCB) and activated B-cell (ABC), using the machine learning techniques described herein.

FIG. 10 is a block diagram of an illustrative computer system that may be used in implementing the machine learning techniques described herein.

FIG. 11 is a block diagram of an illustrative environment 1100 in which the machine learning techniques described herein may be implemented.

FIG. 12 is an exemplary distribution of molecular cancer grade among PAM50 subtypes.

FIG. 13 are illustrative data sets and enrichment signatures showing how progeny process scores correspond to given and predicted cancer grades in TCGA BRCA.

FIG. 14 are exemplary plots comparing different protein expression levels for different predicted cancer grades.

FIG. 15 is an exemplary plot of cytolitic score for different predicted cancer grades.

FIG. 16 are illustrative plots showing the difference in mutations between different cancer grades, according to WES data.

FIG. 17 shows example segments that are differentially amplified or deleted between predicted cancer grades, according to WES data.

FIG. 18 are illustrative data sets and enrichment signatures showing how progeny process scores correspond to given and predicted cancer grades in TCGA KIRC.

FIG. 19 is a plot illustrating chromosomal instability for different cancer grades.

FIG. 20 are plots comparing different protein expression for different predicted cancer grades.

FIG. 21 illustrates genes, according to WES data, that are differentially amplified or deleted between predicted cancer grades.

FIG. 22 illustrates genes, according to WES data, that are differentially amplified or deleted between predicted cancer grades.

FIG. 23A shows example validation data sets, associated cancer grade reported for samples of the data sets, predicted cancer grade obtained using the machine learning techniques described herein, for determining lung adenocarcinoma cancer grade, and the enrichment signatures for different pathways, illustrating gene expression profiles associated with grade 1 and grade 3.

FIG. 23B shows example results of applying validation data sets to a lung adenocarcinoma cancer grade classifier, using the machine learning techniques described herein.

FIG. 23C is an example plot of true positive rate versus false positive rate for predicting cancer grade of different biological samples using the machine learning techniques described herein.

FIG. 24A shows example validation data sets, associated cell of origin reported for samples of the data sets, predicted cell of origin obtained using the machine learning techniques described herein, for determining DLBCL subtype, and the enrichment signatures for ABC and GCB subtypes.

FIG. 24B shows example validation data sets, associated cell of origin reported for samples of the data sets, predicted cell of origin obtained using the machine learning techniques described herein, for determining DLBCL subtype, and the enrichment signatures for ABC and GCB subtypes.

FIGS. 24C and 24D are example plots of survival rates for different groups (ABC, GCB).

FIG. 24E is an example plot of true positive rate versus false positive rate for predicting DLBCL subtype of different biological samples using the machine learning techniques described herein.

FIG. 25A shows example validation data sets, associated HPV status reported for samples of the data sets, predicted HPV status obtained using the machine learning techniques described herein, for determining HPV status, and the enrichment signatures for different pathways, illustrating gene expression profiles associated with HPV status.

FIGS. 25B and 25C are example plots of survival rates for different groups of HPV status (positive HPV and negative HPV).

FIG. 25D is an example plot of true positive rate versus false positive rate for predicting HPV status of different biological samples using the machine learning techniques described herein.

FIG. 25E is an example plot of true positive rate versus false positive rate for predicting HPV status of different biological samples using the machine learning techniques described herein.

FIG. 25F is an example plot illustrating the performance of a classifier for different HPV strains, using the machine learning techniques described herein.

FIG. 26 is a diagram of an illustrative process for determining peripheral T-cell lymphoma (PTCL) subtype of a biological sample using the machine learning techniques described herein.

FIG. 27 is a diagram of an illustrative process for determining peripheral T-cell lymphoma (PTCL) subtype of a biological sample using the machine learning techniques described herein.

FIG. 28 is a diagram of an illustrative process for determining a characteristic of a biological sample based on using multiple statistical models to determine peripheral T-cell lymphoma (PTCL) subtype of the biological sample using the machine learning techniques described herein.

FIG. 29 is a flow chart of an illustrative process for determining a subtype of peripheral T-cell lymphoma (PTCL) for a biological sample using a gene ranking and a statistical model using the machine learning techniques described herein.

FIG. 30 is an example plot of survival rates for the different peripheral T-cell lymphoma (PTCL) subtypes.

DETAILED DESCRIPTION

Characteristics of a biological cell may relate to the expression levels of certain genes. For example, a cancerous cell may have some genes upregulated and other genes downregulated relative to a normal, healthy cell. This relationship between cell characteristics and gene expression levels may be utilized in analyzing gene expression data for biological cells. In particular, such a relationship may provide certain benefits in analyzing characteristics of biological cells that are considered histological characteristics, including tissue of origin and cancer grade, which generally relate to features of biological cells that are observed visually by a person (e.g., pathologist). In some instances, the gene expression data may provide a more consistent assessment of a certain cell characteristic than by using histological techniques, which may be subject to variation between differences in assessment among pathologists.

Large amounts of gene expression data can be obtained through different platforms, including by using a gene expression microarray and by performing next generation sequencing, and is now available or can be generated to characterize biological cells. However, the inventors have recognized that information that is derivable from these data is compromised by differences among different gene sequencing platforms which may lead to variation in gene expression data produced by the sequencing platforms, even if they are used to sequence the same biological sample. For example, microarrays and next generation sequencing (NGS) techniques, may produce gene expression data where the particular values representing gene expression levels may vary among the platforms, even if obtained from the same biological sample. This variation in the expression values across different sequencing platforms may occur because of how the expression data is obtained. The processes and devices used to obtain gene expression data using a particular type of sequencing platform (e.g., next generation sequencing, microarray) may impact the specific values for the expression levels obtained. In turn, the values for the expression levels depend on which sequencing platform was used to obtain the gene expression data. This variation may occur not only across different types of sequencing platforms, but may also occur where the different sequencing platforms are of the same type (e.g., next generation sequencing) and involve different systems (e.g., optical systems, detectors) and processes (e.g., biological sample preparation), or even the same devices in different locations (e.g., due to differences in calibration, use, environment, etc.).

The inventors have recognized that such variation in expression level values presents significant challenges in analyzing gene expression data to characterize cells, especially when using gene expression data obtained using different sequencing platforms. For some expression data, it may be a challenge to normalize the expression level values in such a way so that expression data obtained using different sequencing platforms may be analyzed using the same or similar techniques.

Conventional techniques for analyzing expression data are generally applicable only to analyzing expression data that was obtained using a single sequencing platform and to the specific conditions used in preparing and sequencing the sample. Such conventional techniques are not applicable to analyzing expression data obtained from multiple sequencing platforms, even when the sequencing platforms are of the same type (e.g., next generation sequencing, microarray). For example, conventional techniques for analyzing gene expression data may involve different data analysis pipelines for expression data obtained using different next generation sequencing devices. In addition, some conventional techniques involve implementing different data analysis pipelines depending on how the expression data was obtained even if the same sequencing device was used. For example, conventional techniques for analyzing gene expression data may differ for different sequencing conditions or different sample processing methods. As a result, conventional techniques for analyzing expression data cannot be implemented across different sequencing platforms, sample preparation techniques, and sequencing conditions. This significantly impacts the usability of gene expression data to determine characteristics of cells.

One important group of techniques for analyzing expression data include statistical models (e.g., machine learning models) that are configured to receive expression level values (or a derivative thereof) as input to produce an output of interest such as a prediction or classification. Examples of such statistical models, developed by the inventors, are provided herein. Prior to being used such statistical models are trained on training data comprising pairs of inputs/outputs. If the training data inputs include expression level values (or a derivative thereof) that comes from one type of sequencing platform, then a statistical model trained with such data will exhibit poor performance (on the task for which it is trained) when being provided with expression level values that come from another type of sequencing platform. Indeed, variation across expression level values from different sequencing platforms makes it difficult or impossible to design a single statistical model trained to perform a task using data from any one of multiple types of sequencing platforms. Instead, a separate statistical model would have to be trained for each particular sequencing platform using training data obtained for that particular sequencing platform, which is difficult because it requires training multiple models for each platform and this requires not only additional computational resources, but may simply not be possible as there may not be sufficient training data available for each type of platform.

The inventors have recognized the need for common techniques that can be used for analyzing expression data obtained across different sequencing platforms, despite differences in the type of expression level data generated by the platforms. Such techniques would ease analysis of gene expression data across different subjects, which conventional gene expression level analysis techniques would not allow. For example, techniques described herein for analyzing gene expression data may involve using the same or similar data analysis pipeline (which pipeline may include one or more statistical models, examples of which are provided herein) for expression data obtained using the same type of sequencing platform (e.g., next generation sequencing, microarray) for multiple subjects. Such a data analysis pipeline may allow for expression data to be analyzed in the same or similar manner regardless of sample processing (e.g., DNA extraction, amplification), sequencing conditions (e.g., temperature, pH), data processing (e.g., data processing for next generation sequencing, microarrays) used in obtaining the expression data.

To address some of the difficulties that arise with conventional techniques for analyzing expression data, the inventors have developed improved techniques in analyzing expression data that are independent of the sequencing platform and data processing used to obtain the expression data. In particular, the inventors have recognized that variation of the expression levels among sequencing platforms may be accounted for by using the ranking of a set of genes, rather than the specific values of the expression levels in the data, in subsequent data analysis. For example, the inventors have developed various statistical models for determining various characteristics of a biological sample (e.g., tissue of origin, cancer grade, cancer type for a tissue sample). Each such statistical model is trained to determine a respective characteristic of the biological sample using a ranking of a respective set of genes, rather than using expression levels themselves, which allows the statistical model to operate on expression data obtained from different types of sequencing platforms.

Accordingly, in some embodiments, a statistical model may be used to predict the characteristic(s) of a biological sample based on an input ranking of genes, ranked based on their respective expression levels, for a sequencing platform. Using the input ranking(s), instead of the specific values for the expression levels, allows for the same or similar data processing pipeline to be used across different expression data regardless of the specific manner in which the expression levels were obtained (e.g., regardless of which sequencing platform, sequencing conditions, sample preparation, data processing to obtain expression levels, etc.). As described herein, the statistical model may be specific to the particular characteristic being determined. A statistical model according to the techniques described here may be used to predict one or more characteristics, including cancer grade for cells in the biological sample (e.g., breast cancer grade, kidney clear cell cancer grade, lung adenocarcinoma grade), tissue of origin for cells in the biological sample (e.g., lung, pancreas, stomach, colon, liver, bladder, kidney, thyroid, lymph nodes, adrenal gland, skin, breast, ovary, prostrate, or cell of origin in a tissue such as e.g. germinal center B-cell (GCB) or activated B-cell (ABC)), histological information (tissue type, such as e.g. adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma) for cells in the biological sample, and cancer subtype (e.g. PTCL subtype such as, anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL)), viral status (e.g., HPV status, such as HPV-positive or HPV-negative for head and neck squamous cell carcinoma) for cells in the biological sample.

For example, in some embodiments, rankings of genes based on the gene expression levels (in a biological sample) as determined by a sequencing platform may be provided as input to a statistical model trained to predict tissue of origin for the biological sample. As another example, in some embodiments, rankings of genes based on the gene expression levels (in a biological sample) as determined by a sequencing platform may be provided as input to a statistical model trained to predict cancer grade for the biological sample. In some embodiments, the set of genes being ranked depends on the particular biological characteristic of interest. For example, one set of genes may be used for determining the tissue of origin and another set of genes may be used for determining the cancer grade.

The machine learning techniques that involve using rankings of genes as described herein are an improvement of conventional machine learning technology because they improve over conventional machine learning techniques that use gene expression values directly to analyze gene expression data. For instance, training data obtained using different sequencing platforms may be used in training the statistical models described herein because of the benefits provided by using gene rankings in allowing a common statistical model to be implemented regardless of how the expression data was generated. In contrast, conventional machine learning techniques that involve using gene expression values require individual separate statistical models depending on how the expression data was generated, such as when using different sequencing platforms, sample preparation techniques, etc. Accordingly, the machine learning techniques described herein reduce the need for collecting training data across different sequencing platforms in order to train multiple statistical models required to analyze expression data generated in different ways. In addition, the statistical models described herein may have better performance in contrast to conventional techniques. For instance, a statistical model according to the techniques described herein can be trained using training data obtained from different sources, and thus more training data in general, which improves overall performance of the statistical model being used. In contrast, sources of training data for conventional machine learning models may be limited to a particular sequencing platform, sample preparation technique, etc. and performance may depend on the amount of training data available using a particular way of generating the expression data.

In addition, having a statistical model that is independent of the sequencing platform, sample preparation, and sequencing conditions used may make deployment and use of such a statistical model more practical. In clinical practice, data from different patients is likely to originate from multiple sources, such as expression data generated using different sample preparation techniques and sequencing platforms. As discussed above, the techniques described herein allow for the ability to handle patient data originating from these different sources in a uniform manner by using a common statistical model. The ability to analyze patient data in this way provides improvements to bioinformatics technology that depends on the number of patients represented by the patient data because a larger pool of patients can be analyzed using a common statistical model. These benefits extend to applications where bioinformatics analysis may be used, including predicting characteristic(s) of cells in a biological sample, where being able to use a larger sample size, across many patients, is beneficial.

Moreover, the machine learning techniques described herein may streamline handling of different formats for storing expression data. Different types of sequencing platforms output expression data using different data formats. As discussed herein, a ranking process is used to generate gene rankings, which are then input to a common statistical model. The ranking process may allow for expression data originating from sources that use different data formats to have a similar type of input to the statistical model. This may improve handling of expression data obtained from different sequencing platforms in comparison to conventional analysis techniques where different data processing pipelines are required for different input data formats.

Some embodiments described herein address all of the above-described issues that the inventors have recognized with determining characteristics of a biological sample using gene expression data. However, not every embodiment described herein addresses every one of these issues, and some embodiments may not address any of them. As such, it should be appreciated that embodiments of the technology described herein are not limited to addressing all or any of the above-discussed issues with determining characteristics of a biological sample using gene expression data.

Some embodiments involve obtaining gene expression data for a biological sample of a subject, ranking genes in set(s) of genes based on their expression levels in the expression data to obtain one or more gene rankings. The one or more gene rankings may be used, along with a statistical model, to determine one or more characteristics of the biological sample, including tissue of origin and cancer grade. The statistical model may be trained using rankings of expression levels for some or all genes in the set(s) of genes.

The gene ranking(s) may be obtained by ranking genes in one or more sets of genes based on their expression levels in the expression data. In some embodiments, the expression data includes values, each representing an expression level for a gene in the set(s) of genes. Determining the gene ranking(s) may involve determining a relative rank for each gene in the set(s) of genes based on the values. For example, a first gene ranking may be obtained by ranking genes in a first set of genes based on their expression levels and a second gene ranking may be obtained by ranking genes in a second set of genes based on their expression levels. In some embodiments, the first set of genes and the second set of genes may share some or all genes. Determining the one or more characteristics may involve using the first gene ranking, the second gene ranking, and the statistical model, where the statistical model is trained using training data indicating gene rankings of expression levels for some or all genes in the first set of genes and the second set of genes. Different gene sets may correspond to predicting particular characteristics of the biological sample, and a gene ranking for a specific gene set may be used to determine the characteristic associated with the gene set. For example, a gene ranking where expression levels for a gene set associated with predicting cancer grade may be used to predict cancer grade for cells in the biological sample from which the expression data is obtained.

In some embodiments, the expression data may be obtained for cells in the biological sample, where the subject has or is suspected of having cancer. In the context where tissue of origin is a characteristic being determined, the tissue of origin is for the cells in the biological sample. The tissue of origin may refer to a particular tissue type from which the cells originate, such as lung, pancreas, stomach, colon, liver, bladder, kidney, thyroid, lymph nodes, adrenal gland, skin, breast, ovary, and prostrate.

For example, some embodiments involve using a gene set for predicting tissue of origin, which may include cell of origin, for Diffuse Large B-Cell Lymphoma (DLBCL), such as germinal center B-cell (GCB) and activated B-cell (ABC). Genes in the gene set may be selected from the group consisting of: ITPKB, MYBL1, LMO2, BATF, IRF4, LRMP, CCND2, SLA, SP140, PIM1, CSTB, BCL2, TCF4, P2RX5, SPINK2, VCL, PTPN1, REL, FUT8, RPL21, PRKCB1, CSNK1E, GPR18, IGHM, ACP1, SPIB, HLA-DQA1, KRT8, FAM3C, and HLA-DMB.

In the context where cancer grade is a characteristic being determined, the cancer grade is for the cells in the biological sample. The cancer grade may refer to proliferation and differentiation characteristics of the cells in the biological sample and refer to a numerical grade that is generally determined by visual observation of cells using microscopy, such as Grade 1, Grade 2, Grade 3, and Grade 4. For example, a pathologist may examine a biopsied tissue under a microscope and determine a cancer grade for the tissue. Cancer grades generally depend on the amount of abnormality of the cells in tissue and may depend on the type cancer. For Grade 1, tumor cells and the organization of the tumor tissue appears close to normal, healthy tissue. Grade 1 tumors tend to grow and spread slowly. In contrast, cells and tissue of Grade 3 and Grade 4 tumors do not look like normal cells and tissue. Grade 3 and Grade 4 tumors tend to grow rapidly and spread faster than tumors with a lower grade. An example grading system for cancer tissue is described in American Joint Committee on Cancer AJCC Cancer Staging Manual. 7th ed. New York, N.Y.: Springer; 2010, which is incorporated by reference in its entirety. This grading system applies the following definitions: Grade X (GX) is an undetermined grade and applies when the grade of a tissue cannot be assessed; Grade 1 (G1) is a low grade and applies when the cells are well differentiated; Grade 2 (G2) is an intermediate grade and applies when the cells are moderately differentiated; Grade 3 (G3) is a high grade and applies when the cells are poorly differentiated; and Grade 4 (G4) is a high grade and applies when the cells are undifferentiated.

For example, some embodiments involve using a gene set for predicting breast cancer grade. Genes in the gene set may be selected from the group consisting of: UBE2C, MYBL2, PRAME, LMNB1, CXCL9, KPNA2, TPX2, PLCH1, CCL18, CDK1, MELK, CCNB2, RRM2, CCNB1, NUSAP1, SLC7A5, TYMS, GZMK, SQLE, C1orf106, CDC25B, ATAD2, QPRT, CCNA2, NEK2, IDO1, NDC80, ZWINT, ABCA12, TOP2A, TDO2, S100A8, LAMP3, MMP1, GZMB, BIRC5, TRIP13, RACGAP1, ASPM, ESRP1, MAD2L1, CENPF, CDC20, MCM4, MK167, PBK, CKS2, KIF2C, MRPL13, TTK, BUB1, TK1, FOXM1, CEP55, EZH2, ECT2, PRC1, CENPU, CCNE2, AURKA, HMGB3, APOBEC3B, LAGE3, CDKN3, DTL, ATP6V1C1, KIAA0101, CD2, KIF11, KIF20A, CDCA8, NCAPG, CENPN, MTFR1, MCM2, DSCC1, WDR19, SEMA3G, KCND3, SETBP1, KIF13B, NR4A2, NAV3, PDZRN3, MAGI2, CACNA1D, STC2, CHAD, PDGFD, ARMCX2, FRY, AGTR1, MARCH8, ANG, ABAT, THBD, RAI2, HSPA2, ERBB4, ECHDC2, FST, EPHX2, FOSB, STARD13, ID4, FAM129A, FCGBP, LAMA2, FGFR2, PTGER3, NME5, LRRC17, OSBPL1A, ADRA2A, LRP2, C1orf115, COL4A5, DIXDC1, KIAA1324, HPN, KLF4, SCUBE2, FMO5, SORBS2, CARD10, CITED2, MUC1, BCL2, RGS5, CYBRD1, OMD, IGFBP4, LAMB2, DUSP4, PDLIM5, IRS2, and CX3CR1.

As another example, some embodiments involve using a gene set for predicting kidney clear cell cancer grade. Genes in the gene set may be selected from the group consisting of: PLTP, CIS, LY96, TSKU, TPST2, SERPINF1, SRPX2, SAA1, CTHRC1, GFPT2, CKAP4, SERPINA3, CFH, PLAU, BASP1, PTTG1, MOCOS, LEF1, SLPI, PRAME, STEAP3, LGALS2, CD44, FLNC, UBE2C, CTSK, SULF2, TMEM45A, FCGR1A, PLOD2, C19orf80, PDGFRL, IGF2BP3, SLC7A5, PRRX1, RARRES1, LHFPL2, KDELR3, TRIB3, IL20RB, FBLN1, KMO, C1R, CYP1B1, KIF2A, PLAUR, CKS2, CDCP1, SFRP4, HAMP, MMP9, SLC3A1, NAT8, FRMD3, NPR3, NAT8B, BBOX1, SLC5A1, GBA3, EMCN, SLC47A1, AQP1, PCK1, UGT2A3, BHMT, FMO1, ACAA2, SLC5A8, SLC16A9, TSPAN18, SLC17A3, STK32B, MAP7, MYLIP, SLC22A12, LRP2, CD34, PODXL, ZBTB42, TEK, FBP1, and BCL2.

As another example, some embodiments involve using a gene set for predicting cancer grade for lung adenocarcinoma. Genes in the gene set may be selected from the group consisting of: AADAC, ALDOB, ANXA10, ASPM, BTNL8, CEACAM8, CENPA, CHGB, CHRNA9, COL11A1, CRABP1, F11, GGTLC1, HJURP, IGF2BP3, IHH, KCNE2, KIF14, LRRC31, MYBL2, MYOZ1, PCSK2, PI15, SCTR, SHH, SLC22A3, SLC7A5, SPOCK1, TM4SF4, TRPM8, YBX2.

Some embodiments involve using the machine learning techniques described herein to predict cell of origin for diffuse large B-cell lymphoma (DLBCL) for a biological sample. Such embodiments may involve using a gene set for predicting cell of origin, such as germinal center B-cell (GCB) and activated B-cell (ABC). Genes in the gene set may be selected from the group consisting of: ITPKB, MYBL1, LMO2, BATF, IRF4, LRMP, CCND2, SLA, SP140, PIM1, CSTB, BCL2, TCF4, P2RX5, SPINK2, VCL, PTPN1, REL, FUT8, RPL21, PRKCB1, CSNKIE, GPR18, IGHM, ACP1, SPIB, HLA-DQA1, KRT8, FAM3C, and HLA-DMB.

Some embodiments involve using the machine learning techniques described herein to predict a subtype of peripheral T-cell lymphoma (PTCL) for a biological sample. Such embodiments may involve using a gene set for predicting PTCL subtype, such as, anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL). Genes in the gene set may be selected from the group consisting of: EFNB2, ROBO1, SlPR3, ANK2, LPAR1, SNAP91, SOX8, RAMP3, TUBB2B, ARHGEF10, NOTCH1, ZBTB17, CCNE1, FGF18, MYCN, PTHLH, SMARCA2, WNK1, NKX2-1, CYP26A1, HPSE, CTLA4, PELI1, PRKCB, SPAST, ALS2, KIF3B, ZFYVE27, GF18, FNTB, REL, DMRT1, SLC19A2, STK3, PERP, TNFRSF8, TMOD1, BATF3, CDC14B, WDFEY3, AGT, ALK, ANXA3, BTBD11, CCNA1, DNER, GAS1, HS6ST2, IL1RAP, PCOLCE2, PDE4DIP, SLC16A3, TIAM2, TUBB6, WNT7B, SMOX, TMEM158, NLRP7, ADRB2, GALNT2, HRASLS, CD244, FASLG, KIR2DL4, LOC100287534, KLRD1, SH2D1B, KLRC2, NCAMI, CXCR5, IL6, ICOS, CD40LG, CD84, IL21, BCL6, MAF, SH2D1A, IL4, PTPN1, PIM1, ENTPD1, IRF4, CCND2, IL16, ETV6, BLNK, SH3BP5, FUT8, CCR4, GATA3, IL5, IL10, IL13, MMEITPKB, MYBL1, LRMP, KIAA0870, LMO2, CR1, LTBR, PDPN, TNFRSF1A, FCER2, ICAM1, FCGR2B, IKZF2, CCR8, TNFRSF18, IKZF4, FOXP3, IL2, TBX21, IFNG, GZMH, GNLY, EOMES, NCRI, GZMB, NKG7, FGFBP2, KLRF1, CD160, KLRK1, CD226, NCR3, TNFRSF8, BATF3, TMOD1, TMEM158, MSC, POPDC3.

Some embodiments involve using the machine learning techniques described herein to predict a viral status for a biological sample. In some embodiments the viral status is human papillomavirus (HPV) status (e.g., HPV-positive status, HPV-negative status) for a biological sample. In some embodiments, the HPV status may be determined for a subject having, suspected of having, or at risk of having head and neck squamous cell carcinoma. Genes in the gene set may be selected from the group consisting of: APOBEC3B, ATAD2, BIRC5, CCL20, CCND1, CDC45, CDC7, CDK1, CDKN2A, CDKN2C, CDKN3, CENPF, CENPN, CXCL14, DCN, DHFR, DKK3, DLGAP5, EPCAM, FANCI, FEN1, GMNN, GPX3, ID4, IGLC1, IL18, IL1R2, KIF18B, KIF20A, KIF4A, KLK13, KLK7, KLK8, KNTC1, KRT19, LAMP3, LMNB1, MCM2, MCM4, MCM5, ME1, MELK, MKI67, MLF1, MMP12, MTHFD2, NDN, NEFH, NEK2, NUP155, NUP210, NUSAP1, PDGFD, PLAGL1, PLOD2, PPPIR3C, PRIM1, PRKDC, PSIP1, RAD51AP1, RASIPI, RFC5, RNASEH2A, RPA2, RPL39L, RSRC1, RYR1, SLC35G2, SMC2, SPARCL1, STMN1, SYCP2, SYNGR3, TIMELESS, TMPO, TPX2, TRIP13, TYMS, UCP2, UPF3B, USP1, ZSCAN18.

It should be appreciated that the various aspects and embodiments described herein be used individually, all together, or in any combination of two or more, as the technology described herein is not limited in this respect.

FIG. 1 is a diagram of an illustrative processing pipeline 100 for determining one or more characteristics (e.g. tissue of origin, cancer grade, PTCL subtype) of a biological sample based on one or more respective gene rankings for the biological sample, which may include ranking genes based on their gene expression levels and using the ranking(s) and one or more statistical models to determine the one or more characteristics, in accordance with some embodiments of the technology described herein. Processing pipeline 100 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, processing pipeline 100 may be performed by a desktop computer, a laptop computer, a mobile computing device. In some embodiments, processing pipeline 100 may be performed within one or more computing devices that are part of a cloud computing environment.

As shown in FIG. 1 , gene expression data 102 may be obtained for a biological sample of a subject. The subject may have, be suspected of having, or be at risk of having cancer (e.g., breast cancer, kidney cancer, clear cell kidney cancer, lymphoma). A subject having, suspected of having, or at risk of having cancer may be a subject exhibiting one or more signs or symptoms of cancer, subject that is diagnosed as having cancer, a subject that has a family history and/or a genetic predisposition to having cancer, and/or a subject that has one or more other risk factors for cancer (e.g., age, exposure to carcinogens, environmental exposure, exposure to a virus associated with a higher likelihood of developing cancer, etc.). Expression data 102 may be obtained using any suitable sequencing platform (e.g., gene expression microarray, next generation sequencing, hybridization-based expression assay), resulting in expression data (e.g., microarray data, RNAseq data, hybridization-based expression assay data) for the biological sample. Some embodiments involve performing a sequencing process of the biological sample (e.g., a gene expression microarray, next generation sequencing) prior to obtaining expression data 102. In some embodiments, obtaining gene expression data 102 may involve obtaining gene expression data 102 in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way. In some embodiments, obtaining gene expression data 102 may involve analyzing a biological sample (in vitro) and accessing (e.g., by a computing device, by a processor) the expression data. Further aspects relating to obtaining expression data are provided in the section titled “Obtaining Expression Data”.

As shown in FIG. 1 , expression data 102 includes expression level values for N different genes, “gene1”, “gene2”, “gene3”, . . . “geneN” of “sample 1.” Different sequencing platforms may be used to obtain expression data 102. In some embodiments, expression data 102 may be obtained using a gene expression microarray (e.g., by determining an amount of RNA that binds to different probes on a microarray). A gene expression microarray may detect expression of thousands of genes at a time. Expression data 102 associated with using a gene expression microarray may be associated with 1,000, at least 10,000, or at least 100,000 gene detection events. In some embodiments, expression data 102 may be obtained by performing next generation sequencing. Such expression data may be associated with obtaining sequence reads using next generation sequencing, aligning the sequencing reads to a reference (e.g., by using one or more sequence alignment algorithms), determine expression level values for certain genes based on the alignment, etc. Expression data 102 associated with performing next generation sequencing may be associated with at least 10,000, at least 100,000, at least 1,000,000, or at least 10,000,000 sequence reads. In some embodiments, expression data 102 may be obtained by using a hybridization-based expression assay (e.g., labeled probe to target a region of interest in a biological sequence). Expression data 102 associated with using a hybridization-based expression may be associated with 1,000, at least 10,000, or at least 100,000 gene detection events.

In some embodiments, expression data 102 includes RNA Seq data. In such embodiments, expression data 102 may involve obtaining RNA expression levels obtained by performing RNA sequencing. In some embodiments, expression data 102 is obtained by performing whole genome sequencing (WGS). In some embodiments, expression data 102 is obtained by performing whole exome sequencing (WES). In some embodiments, expression data 102 includes a combination of RNA Seq data and WGS data. In some embodiments, expression data 102 includes a combination of RNA Seq data and WES data.

In some embodiments, expression data 102 includes values for the N different genes, where a value represents an expression level for a particular gene. For example, first expression data 102 includes a value of 10.455 representing the expression level for gene2 and a value of 0.0001 representing the expression level for geneN, which indicates that gene2 has a higher expression level in sample 1 than geneN. As discussed above, the sequencing platform used to obtain expression data 102 may impact the specific values of the expression data and the relative values among the genes.

According to some embodiments, ranking process 108 may involve ranking genes based on their expression levels in expression data 102 to obtain gene ranking(s) 110. Ranking process 108 may involve ranking genes in a set of genes based on numerical values of their expression levels. In some embodiments, ranking process 108 may involve ranking some or all of the genes in expression data 102 to obtain gene ranking(s) 110. Different gene rankings may be obtained by ranking expression levels for different gene sets. Determining a gene ranking may involve determining a relative rank for each gene in the set of genes. As shown in FIG. 1 , genes in expression data 102 may be ranked based on their expression levels using ranking process 108 for gene set 1 106 a to obtain first gene ranking 110 a. Similarly, genes in expression data 102 may be ranked based on their expression levels using ranking process 108 for gene set 2 106 b to obtain second gene ranking 110 b. Gene ranking 110 a and gene ranking 110 b have relative ranks for the different genes. As shown in FIG. 1 , gene ranking 110 a has the relative ranks of 30, N−1, 2, and 1, for gene1, gene2, gene3, and geneN, respectively, and gene ranking 110 b has the relative ranks of 15, 21, 2, and 1, for gene1, gene2, gene3, and geneN, respectively. A gene ranking may include values identifying the relative ranks for genes in the gene ranking. In some embodiments, the values identifying the relative ranks may include ordinal numbers. In some embodiments, the values identifying the relative ranks may include whole numbers, such as shown in FIG. 1 . In some embodiments, the values identifying the relative ranks may be used as an input (e.g., a vector of the relative ranks) to a statistical model for predicting a characteristic using the techniques described herein. In some embodiments, a gene ranking may include a sorted list of genes according to the relative ranks of the genes. In such embodiments, the sorted list of genes may be used as an input (e.g., a vector with the sorted list of genes) to a statistical model for predicting a characteristic using the techniques described herein. For example, a gene set may include gene list A=[x1, x2, x3, . . . xN−1, xN] and ranking process 108 may output a sorted list of genes [x2, x15, xN−1 . . . x1, xN] with their corresponding relative ranks as [1, 2, 3, . . . N−1, N]. The sorted list of genes [x2, x15, xN−1 . . . x1, xN] and their relative ranks [1, 2, 3, . . . N−1, N] may be used as input to a statistical model.

In some embodiments, ranking process 108 may involve ordering genes in the gene set from the lowest to highest expression level and labeling the list of genes with the rank for individual genes. For example, lowest expression level values are ordered first on the list of genes and their corresponding labels are lowest (e.g., 1, 2, 3, etc.) while the highest expression level values have corresponding higher labels. In some embodiments, ranking process 108 may involve ordering genes in descending order so that genes in the gene set are ranked from highest to lowest expression level values. In some embodiments, ranking process 108 may involve one or more pre-processing steps prior to ranking genes, including binning gene expression values, rounding gene expression values. For example, in some embodiments, gene expression values may be sorted into bins and then ranked. As another example, in some embodiments, gene expression values may be truncated and then ranked. Other pre-processing steps may be applied to the expression levels and the ranking may be performed on the pre-processed values, as aspects of the technology described herein are not limited to ranking only by sorting on the exact gene expression levels that were obtained.

In instances where a group of genes have equal or substantially similar expression level values, the genes in the group may have a common rank and a label indicating the common rank. In some embodiments, the common rank may be determined as being the average of the ranks for the genes in the group. For example, one gene in the gene set may have an expression level value of 30 and is ranked as 4 and the next genes in the ordered list have expression level values of 35, 35, and 35, which are ranked as 5, 6, and 7, respectively, then these genes are all ranked as 6 (which is the average of 5, 6, and 7). In some embodiments, a gene ranking may include two or more genes having a common rank. In some embodiments, a gene ranking where a group of genes have a common rank may include consecutive ranking labels (e.g., 1, 2, 2, 2, 3, 4, 5, etc.). In some embodiments, a gene ranking where a group of genes have a common rank may include ranking labels that skip one or more values (e.g., 1, 2, 2, 2, 5, 6, 6, 8, etc.). In some embodiments, a group of genes having equal or substantially similar expression level values may be ranked according to the minimum rank or maximum rank in the group of genes.

To determine a particular characteristic of a biological sample (e.g., tissue of origin, cancer grade, tissue type, tissue subtype, such as e.g., PTCL subtype, viral status, such as e.g., HPV status), a selected set of genes may be used in ranking process 108 to obtain gene ranking(s) 110. As shown in FIG. 1 , gene set 1 106 a is used to obtain gene ranking 110 a, which is then used to determine characteristic 1 114 a. Similarly, gene set 2 106 b is used to obtain gene ranking 110 b, which is then used to determine characteristic 2 114 b. For example, one set of genes may be used for determining tissue of origin for the biological sample and another set of genes may be used for determining cancer grade.

The number of genes in a set of genes may be in the range of 3 to 1,000 genes, 5 to 500 genes, 5 to 200 genes, 5 to 100 genes, 3 to 50 genes, 20 to 100 genes, 50 to 100 genes, 50 to 200 genes, 50 to 300 genes, 100 to 300 genes, and 50 to 500 genes. The set of genes may include at least 3 genes, at least 5 genes, at least 10 genes, or at least 20 genes. The set of genes may consist of 5-50 genes, 5-100 genes, 20-100 genes, 50-100 genes, 5-200 genes, 5-300 genes, 10-200 genes, 50-300 genes, 5-500 genes, or 50-500 genes.

A gene ranking and a statistical model may be used to determine a particular characteristic of the biological sample. In particular, a gene ranking may be used as an input to the statistical model and an output indicating the characteristic may be obtained. To obtain different characteristics, different gene sets and different statistical models are used where determining a particular characteristic involves using a specific gene set and a statistical model trained using training data indicating rankings of expression levels for some or all genes in the set of genes. For example, statistical model 112 a is specific for determining characteristic 1 114 a and was trained using training data indicating rankings of expression levels for some or all of the genes in gene set 1 106 a. Similarly, statistical model 112 b is specific for determining characteristic 2 114 b and was trained using training data indicating rankings of expression levels for some or all of the genes in gene set 2 106 b. For example, statistical model 112 a and gene set 1 106 a may be used for determining cancer grade for cells in the biological sample and statistical model 112 b and gene set 2 106 b may be used for determining tissue of origin for cells in the biological sample.

The training data may include rankings of expression levels associated with multiple samples, including samples associated with the characteristic being determined using the statistical model. For example, in embodiments where the statistical model is used to predict cancer grade, the training data may include rankings of expression levels associated with samples of multiple cancer grades (e.g., Grade 1, Grade 2, Grade 3). As another example, in embodiments where the statistical model is used to predict tissue of origin, the training data may include rankings of expression levels associated with samples from multiple tissue of origins (e.g., thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue). As another example, in embodiments where the statistical model is used to predict HPV status, the training data may include rankings of expression levels associated with samples from both HPV-positive status and HPV-negative status. As another example, in embodiments where the statistical model is used to predict PTCL subtype, the training data may include rankings of expression levels associated with samples from different PTCL subtypes (e.g., adult T-cell leukemia/lymphoma (ATLL), angioimmunoblastic T-cell lymphoma (AITL), NK/T-cell lymphoma (NKTCL), anaplastic large cell lymphoma (ALCL), and cases belong to the Not Otherwise Specified (PTCL-NOS)).

It should be appreciated that a statistical model, such as statistical model 112 a and statistical model 112 b, may be used determining one or more characteristics for different biological samples obtained from different subjects. In some instances, the number of subjects that may use the same statistical model may be at least 50, 100, 200, 300, 500, 1,000, 2,000, 5,000, 10,000, or more. Using the statistical model for different subjects may ease analysis of expression data across the different subjects because the same data processing pipeline may be implemented for the individual subjects.

In some embodiments, ranking process 108 may only rank genes included in a set of genes such that not all of the genes in the expression data may obtain a rank or be included in a gene ranking. In such embodiments, the ranking is specific to the set of genes and may be used as an input to statistical model 112.

In some embodiments, ranking process 108 may involve ranking all the genes in expression data 102, such that each gene has a respective rank. In such embodiments, the ranking includes genes outside the set of genes. In some embodiments, an input to a statistical model may include the ranks, determined by ranking process 108, for the set of genes. In some embodiments, an input to a statistical model may include the ranking obtained by ranking process 108 and a statistical model may selectively use the ranks for the set of genes in the ranking as part of determining the one or more characteristics.

A statistical model may involve using one or more suitable machine learning algorithms, including one or more classifiers. Examples of classifiers that a statistical model may include are a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier. In some embodiments, a statistical model may involve using a gradient boosted decision tree classifier. In some embodiments, a statistical model may involve using a decision tree classifier. In some embodiments, a statistical model may involve using a gradient boosted classifier. In some embodiments, a statistical model may involve using a random forest classifier. In some embodiments, a statistical model may involve using a clustering-based classifier. In some embodiments, a statistical model may involve using a Bayesian classifier. In some embodiments, a statistical model may involve using a Bayesian network classifier. In some embodiments, a statistical model may involve using a neural network classifier. In some embodiments, a statistical model may involve using a kernel-based classifier. In some embodiments, a statistical model may involve using a support vector machine classifier.

In some embodiments, a statistical model may perform binary classification of one or more features as an output of the statistical model. For example, such a statistical model may perform classification of one or more cancer grades (e.g., Grade 1, Grade 2, Grade 3) and an output of the statistical model may include a prediction for each of the one or more cancer grades indicating whether a biological sample is categorized as being a particular cancer grade.

In some embodiments, a statistical model may involve using a machine learning algorithm that implements of a gradient boosting framework, such as a gradient boosting decision tree (GBDT) and a gradient boosted regression tree (GBRT). An example of a machine learning algorithm that implements a gradient boosting decision tree is the LightGBM package, which is further described in Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye and Tie-Yan Liu, LightGBM: A highly efficient gradient boosting decision tree, Advances in Neural Information Processing Systems, pp. 3149-3157, 2017, which is incorporated by reference herein in its entirety. An example of a machine learning algorithm that implements a gradient boosting framework is the XGBoost package, which is further described in Tiangi Chen and Carlos Guestrin. XGBoost: A scalable tree boosting system, In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, ACM, 2016, which is incorporated by reference herein in its entirety. An example of a machine learning algorithm that implements a gradient boosted regression tree is the pGBRT package, which is further described in Stephen Tyree, Kilian Q Weinberger, Kunal Agrawal, and Jennifer Paykin, Parallel boosted regression trees for web search ranking, In Proceedings of the 20th international conference on World wide web, pp. 387-396, ACM, 2011, which is incorporated by reference herein in its entirety.

A statistical model may be trained using multiple rankings of expression levels for some or all of the genes in the set of genes. Training data may include available expression data obtained through research organizations, including the National Cancer Institute (NCI) (e.g., Gene Expression Omnibus (GEO)), National Center for Biotechnology Information (NCBI) (e.g., Sequence Reach archive (SRA)), The Cancer Genome Atlas Program (TCGA), ArrayExpress Archive of Functional Genomics Data (by the European Molecular Biology Laboratory), and International Cancer Genome Consortium.

For example, a statistical model used for determining cancer grade for breast cancer may be trained using data from Series GSE96058 available through the NCI. As another example, a statistical model used for determining cancer grade for kidney clear cell cancer may be trained using data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data collection. As yet another example, a statistical model used for determining tissue of origin for DLBCL (e.g., ABC, GCB) may be trained using data from one or more of Series GSE117556, Leipzig Lymphoma data set (10.1186/s13073-019-0637-7), Series GSE31312, Series GSE10846, Series GSE87371, Series GSE11318, Series GSE32918, Series GSE23501, Lymphoma/Leukemia Molecular Profiling Project (LLMPP), and Series GSE93984. As another example, a statistical model used for determining tissue of origin and histological information (e.g., tissue type) for cancer may be trained using data from The Cancer Genome Atlas Program (TCGAP).

One characteristic that may be determined using the techniques described herein is cancer grade for cells in the biological sample. Cancer grade may include Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5. It should be appreciated that some cancer grading systems may include any suitable number of grades, or other scores, and that the techniques described herein may be used for determining any number of cancer grades regardless of the cancer grading system being implemented. For example, some cancer grading systems may have a number of cancer grades in the range of 1 to 10. Another characteristic is tissue of origin for cells in the biological sample. Tissue of origin may include lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue. In some instances, tissue of origin may refer to cell of origin. For example, where the subject has, is suspected of having, or is at risk of having Diffuse Large B-Cell Lymphoma (DLBCL), the tissue of origin is a cell of origin may include germinal center B-cell (GCB) and activated B-cell (ABC).

Another characteristic is histological information for cells in the biological sample. The histological information may correspond to a determination made by a physician (e.g., pathologist) using microscopy to visually inspect the biological sample. Histological information may include a tissue type. Examples of tissue types include adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma. In some embodiments, a statistical model may output a combination of tissue of origin and histological information. The combination of tissue of origin and histological information may include lung adenocarcinoma, lung squamous cell carcinoma, melanoma, breast carcinoma, colorectal adenocarcinoma, ovarian serous cystadenocarcinoma, phenochromocytoma, bladder urothelial carcinoma, cervical squamous cell carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, paraganglioma, prostate adenocarcinoma, sarcoma, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma.

A characteristic (e.g., cancer grade, tissue of origin, PTCL subtype) may be output to a user, such as a physician or clinician, by displaying the characteristic to the user in a graphical user interface (GUI), including the characteristic in a report, sending an email to the user, and/or in any other suitable way. The subject's characteristic may be used for various clinical purposes, including assessing the efficacy of a treatment for cancer, identifying a treatment for the subject, administering a treatment for the subject, determining a prognosis for the subject, and/or evaluating suitability of the subject for participating in a clinical trial. In some embodiments, the subject's characteristic may be used in identifying a treatment for the subject. For example, in embodiments where a tissue of origin is determined for cells in the biological sample, the determined tissue of origin may be used to identify a treatment for the subject associated with treating cancers of the determined tissue of origin. As yet another example, in embodiments where a cancer grade is determined for cells in the biological sample, the determined cancer grade may be used to identify a treatment for the subject associated with treating cancers having the determined cancer grade. As yet another example, in embodiments where a PTCL subtype is determined for cells in the biological sample, the determined PTCL subtype may be used to identify a treatment for the subject suitable for treating lymphomas of the determined PTCL subtype. In turn, the identified treatment may be administered.

In some embodiments, the subject's characteristic may be used for administering a treatment for the subject. For example, in embodiments where a tissue of origin is determined for cells in the biological sample, a physician may administer a treatment for the subject associated with treating cancers of the determined tissue of origin. As yet another example, in embodiments where a cancer grade is determined for cells in the biological sample, a physician may administer a treatment for the subject associated with treating cancers having the determined cancer grade. As yet another example, in embodiments where a PTCL subtype is determined for cells in the biological sample, a physician may administer a treatment for the subject suitable for treating lymphomas of the determined PTCL subtype. Further examples where characteristics of a biological sample determined using the techniques described herein are used for administering a treatment are provided in the section titled “Methods of Treatment”.

In some embodiments, the subject's characteristic may be used in determining a prognosis for the subject. In embodiments where the subject has, is suspected of having, or is at risk of having cancer (e.g., kidney cancer, clear cell kidney cancer, lymphoma, head and neck squamous cell carcinoma, lung adenocarcinoma), the determined subject's characteristic may be used to determine a prognosis for the subject. For example, in embodiments, where the subject's characteristic is cancer grade, the determined cancer grade (e.g., Grade 1, Grade2, Grade3) may be used to determine a prognosis for the subject. Further aspects relating to other applications where characteristics of a biological sample determined using the techniques described herein are provided in the section titled “Applications”.

In some embodiments, the determined characteristic of the biological sample may include cancer grade for cells in the biological sample. In such embodiments, the set of genes used to obtain a gene ranking may include genes associated with biological features, expression pathways, or otherwise associated with determining cancer grade. Some embodiments involve using a gene set for determining cancer grade for breast cancer. Examples of genes that may be included in such a gene set are listed in Table 1, below.

TABLE 1 Grade Classifier for Breast Cancer NCBI Gene Gene ID NCBI Accession Number(s) UBE2C 11065 NM_001281741; NM_001281742; NM_007019; NM_181799; NM_181800; NM_181801 MYBL2 4605 NM_001278610; NM_002466 PRAME 23532 NM_001291715; NM_001291716; NM_006115; NM_206953; NM_206954; NM_206955; NM_206956; NM_001291717; NM_001291719; NM_001318126; NM_001318127 LMNB1 4001 NM_005573 CXCL9 4283 NM_002416 KPNA2 3838 NM_001320611; NM_002266 TPX2 22974 NM_012112; XM_011528697; XM_011528699 PLCH1 23007 NM_001130960; NM_001130961; NM_014996; XM_011512561; XM_017005923; XM_017005926; NM_001349251; XM_011512560; XM_011512562; XM_011512565; XM_017005925; NM_001349250; NM_001349252; XM_005247238; XM_005247239; XM_011512567; XM_017005927; XM_011512566 CCL18 6362 NM_002988 CDK1 983 NM_001320918; NM_001786; NM_033379; XM_005270303 MELK 9833 NM_001256685; NM_001256687; NM_001256688; NM_001256689; NM_001256690; NM_001256692; NM_001256693; NM_014791; XM_011518076; XM_011518077; XM_011518078; XM_011518079; XM_011518081; XM_011518082; XM_011518083; XM_011518084 CCNB2 9133 NM_004701 RRM2 6241 NM_001034; NM_001165931 CCNB1 891 NM_031966 NUSAP1 51203 NM_001243142; NM_001243143; NM_001243144; NM_001301136; NM_016359; NM_018454; XM_005254430 SLC7A5 8140 NM_003486 TYMS 7298 NM_001071 GZMK 3003 NM_002104 SQLE 6713 NM_003129 C1orf106 55765 NM_001142569; NM_018265; XM_011509754; XM_011509755 CDC25B 994 NM_001287519; NM_001287520; NM_004358; NM_021872; NM_021873; NM_001287516; NM_001287522; NM_001287518; NM_001287524 ATAD2 29028 NM_014109 QPRT 23475 NM_001318249; NM_001318250; NM_014298 CCNA2 890 NM_001237 NEK2 4751 NM_001204182; NM_001204183; NM_002497; XM_005273147 IDO1 3620 NM_002164 NDC80 10403 NM_006101 ZWINT 11130 NM_001005413; NM_007057; NM_032997 ABCA12 26154 NM_015657; NM_173076 TOP2A 7153 NM_001067 TDO2 6999 NM_005651 S100A8 6279 NM_001319197; NM_001319198; NM_001319201; NM_002964 LAMP3 27074 NM_014398 MMP1 4312 NM_002421 GZMB 3002 NM_001346011; NM_004131 BIRC5 332 NM_001168; NM_001012271; NM_001012270 TRIP13 9319 NM_004237; XM_011514163 RACGAP1 29127 NM_001126103; NM_001126104; NM_001319999; NM_001320000; NM_001320001; NM_001320002; NM_001320003; NM_001320004; NM_013277; XM_006719359; XM_011538238; XM_017019220; NM_001320005; NM_001320006; NM_001320007 ASPM 259266 NM_001206846; NM_018136 ESRP1 54845 NM_001122827; NM_001034915; NM_001122825; NM_001122826; NM_017697; XM_005250991 MAD2L1 4085 NM_002358 CENPF 1063 NM_016343; XM_017000086 CDC20 991 NM_001255 MCM4 4173 NM_005914; NM_182746 MKI67 4288 NM_001145966; NM_002417 PBK 55872 NM_001278945; NM_018492; NM_001363040 CKS2 1164 NM_001827 KIF2C 11004 NM_001297655; NM_001297656; NM_001297657; NM_006845 MRPL13 28998 NM_014078 TTK 7272 NM_001166691; NM_003318; XM_011536099; XM_011536100 BUB1 699 NM_001278617; NM_004336 TK1 7083 NM_001346663; NM_003258 FOXM1 2305 NM_001243088; NM_202003; NM_202002; NM_001243089; NM_021953 CEP55 55165 NM_001127182; NM_018131 EZH2 2146 NM_004456; XM_011515884; NM_001203247; NM_001203248; NM_001203249; NM_152998 ECT2 1894 NM_001258315; NM_001258316; NM_018098; XM_011512514 PRC1 9055 NM_003981; NM_001267580 CENPU 79682 NM_024629 CCNE2 9134 NM_057749; XM_011517366; NM_004702; XM_017013959; XM_017013958; NM_057735 AURKA 6790 NM_001323303; NM_001323304; NM_001323305; NM_003600; NM_198433; NM_198434; NM_198435; NM_198436; NM_198437; XM_017028035 HMGB3 3149 NM_001301228; NM_001301229; NM_001301231; NM_005342 APOBEC3B 9582 NM_001270411; NM_004900 LAGE3 8270 NM_006014 CDKN3 1033 NM_001258 DTL 51514 NM_001286229; NM_016448 ATP6V1C1 528 NM_001695 KIAA0101 9768 NM_001029989; NM_014736 CD2 914 NM_001328609; NM_001767 KIF11 3832 NM_004523 KIF20A 10112 NM_005733 CDCA8 55143 NM_001256875; NM_018101 NCAPG 64151 NM_022346 CENPN 55839 NM_001100624; NM_001100625; NM_001270473; NM_001270474; NM_018455; XM_006721236; XM_017023456 MTFR1 9650 NM_014637; NM_001145838 MCM2 4171 NM_004526 DSCC1 79075 NM_024094 WDR19 57728 NM_001317924; NM_025132 SEMA3G 56920 NM_020163 KCND3 3752 NM_172198; XM_011541425; XM_006710632; XM_011541428; NM_001378969; NM_004980; NM_001378970; XM_006710629; XM_006710631; XM_017001245; XM_011541426; XM_011541427; XM_017001244 SETBP1 26040 NM_001130110; NM_015559 KIF13B 23303 NM_015254 NR4A2 4929 NM_006186; XM_017004219; XM_017004220; XR_001738751; XR_001738752; NM_173173; NM_173171; XM_011511246; NM_173172; XR_427087; XM_005246621; XM_006712553 NAV3 89795 XM_017020172; NM_001024383; NM_014903; XM_011538944 PDZRN3 23024 NM_001303140; NM_001303142; NM_001303139; NM_001303141; NM_015009 MAGI2 9863 NM_001301128; NM_012301 CACNA1D 776 XM_005265448; NM_000720; NM_001128839; NM_001128840 STC2 8614 NM_003714 CHAD 1101 NM_001267 PDGFD 80310 NM_025208; NM_033135 ARMCX2 9823 NM_001282231; NM_014782; NM_177949; XM_005278109; XM_005278110; XM_005278111; XM_005278113; XM_005278114; XM_005278115; XM_005278116; XM_005278117; XM_011531071; XM_011531072; XM_017029987; XM_017029988; XM_017029989; XM_017029990; XM_017029991; XM_017029992; XM_017029993; XM_017029994; XM_017029995; XM_017029996; XM_017029997 FRY 10129 NM_023037 AGTR1 185 NM_032049; NM_004835; NM_031850; NM_000685; NM_009585 MARCH8 220972 NM_001002266; NM_145021; XM_011539495; XM_006717704; NM_001002265; XR_246519; NM_001282866; XM_005271804; XM_011539492 ANG 283 NM_001097577; NM_001145 ABAT 18 NM_000663; NM_001127448; NM_020686; NM_001386602; NM_001386609; NM_001386611; NM_001386612; NM_001386613; NM_001386605; NM_001386610; NM_001386616; NM_001386601; NM_001386603; NM_001386604; NM_001386606; NM_001386615; NM_001386600; NM_001386607; NM_001386614; NM_001386608 THBD 7056 NM_000361 RAI2 10742 NM_001172739; NM_001172743; NM_021785; NM_001172732; XM_006724459; XM_006724460; XM_011545439; XM_011545440; XM_011545441 HSPA2 3306 NM_021979 ERBB4 2066 NM_001042599; NM_005235; XM_005246376; XM_005246377 ECHDC2 55268 NM_001198962; NM_001198961; NM_018281; XM_011541722; XM_011541726; XM_017001638; XM_024448158; XM_011541719; XM_011541723; XM_024448164; XR_002957011; NM_001319958; XR_002957012; XM_011541709; XM_024448153; XM_024448157; XR_002957014; XM_011541713; XM_024448163; XM_011541715; XM_017001640; XM_024448160; XM_011541720; XM_011541727; XM_024448159; XM_024448161; XR_002957013; XM_017001639; XM_024448152 FST 10468 NM_013409; NM_006350 EPHX2 2053 NM_001256482; NM_001256483; NM_001256484; NM_001979 FOSB 2354 NM_006732; XM_005258691; NM_001114171 STARD13 90627 NM_178006; NM_052851; NM_001243466; NM_001243474; NM_001243476; NM_178007 ID4 3400 NM_001546 FAM129A 116496 NM_052966 FCGBP 8857 NM_003890 LAMA2 3908 NM_000426; NM_001079823 FGFR2 2263 NM_001144919; NM_023029; NM_000141; NM_001144913; NM_001144914; NM_001144915; NM_001144916; NM_001144917; NM_001144918; NM_001320654; NM_001320658; NM_022970 PTGER3 5733 NM_198718; NM_001126044; NM_198714; NM_198715; NM_198716; NM_198717; NM_198719 NME5 8382 NM_003551 LRRC17 10234 NM_001031692; NM_005824; XM_005250108 OSBPL1A 114876 NM_080597; NM_018030; XM_017025533; XR_002958162; XM_006722380; XM_017025530; NM_133268; XM_017025532; XR_001753139; NM_001242508; XM_006722382; XM_017025531 ADRA2A 150 NM_000681 LRP2 4036 NM_004525 C1orf115 79762 NM_024709 COL4A5 1287 NM_000495; NM_033380 DIXDC1 85458 NM_001037954; NM_001278542; NM_033425 KIAA1324 57535 NM_001267048; NM_020775; XM_011541825 HPN 3249 NM_002151; NM_182983; XM_017026732; NM_001384133; XM_017026731; NM_001375441 KLF4 9314 NM_001314052; NM_004235 SCUBE2 57758 NM_001170690; NM_001330199; NM_020974 FMO5 2330 NM_001461; XM_005272946; XM_005272947; XM_005272948; XM_011509350; NM_001144829; NM_001144830; XM_006711245 SORBS2 8470 NM_001145670; NM_001145671; NM_001145672; NM_001145673; NM_001145674; NM_001270771; NM_003603; NM_021069; XM_005263312; XM_006714390; XM_017008771 CARD10 29775 NM_014550 CITED2 10370 NM_001168389; NM_001168388; NM_006079 MUC1 4582 NM_001204292; NM_001204286; NM_001204291; NM_001204285; NM_001204287; NM_001204288; NM_001204289; NM_001204290; NM_001204295; NM_001204297; NM_001204296; NM_001018016; NM_001018017; NM_001044390; NM_001044391; NM_001044392; NM_001044393; NM_001204293; NM_001204294; NM_002456 BCL2 596 NM_000633; NM_000657 RGS5 8490 NM_003617; NM_001195303; NM_001254748; NM_001254749 CYBRD1 79901 NM_001127383; NM_001256909; NM_024843 OMD 4958 NM_005014 IGFBP4 3487 NM_001552 LAMB2 3913 NM_002292; XM_005265127 DUSP4 1846 NM_001394; NM_057158; XM_011544428 PDLIM5 10611 NM_006457; NM_001011515; NM_001011516; NM_001256429 IRS2 8660 NM_003749 CX3CR1 1524 NM_001171171; NM_001171172; NM_001171174; NM_001337

Some embodiments involve using a gene set for determining cancer grade for kidney clear cell cancer. Examples of genes that may be included in such a gene set are listed in Table 2, below.

TABLE 2 Grade Classifier for Kidney Clear Cell NCBI Gene Gene ID NCBI Accession Number(s) PLTP 5360 NM_001242920; NM_001242921; NM_006227; NM_182676 C1S 716 NM_001346850; NM_001734; NM_201442; XM_005253760 LY96 23643 NM_001195797; NM_015364 TSKU 25987 NM_001258210; NM_001318477; NM_001318478; NM_001318479; NM_015516 TPST2 8459 NM_001008566; NM_003595 SERPINF1 5176 NM_001329903; NM_002615 SRPX2 27286 NM_014467 SAA1 6288 NM_000331; NM_001178006; NM_199161 CTHRC1 115908 NM_001256099; NM_138455 GFPT2 9945 NM_005110 CKAP4 10970 NM_006825 SERPINA3 12 NM_001085 CFH 3075 NM_001014975; NM_000186 PLAU 5328 NM_002658; NM_001145031; NM_001319191 BASP1 10409 NM_001271606; NM_006317 PTTG1 9232, 10744 NM_001282382; NM_001282383; NM_004219 MOCOS 55034 NM_017947 LEF1 51176 NM_001130713; NM_001130714; NM_001166119; NM_016269 SLPI 6590 NM_003064 PRAME 23532 NM_001291715; NM_001291716; NM_006115; NM_206953; NM_206954; NM_206955; NM_206956; NM_001291717; NM_001291719; NM_001318126; NM_001318127 STEAP3 55240 NM_001008410; NM_018234; XM_006712614; XM_006712615; XM_011511403; NM_138637; NM_182915 LGALS2 3957 NM_006498 CD44 960 NM_000610; NM_001001389; NM_001001390; NM_001001391; NM_001001392; NM_001202555; NM_001202556; NM_001202557; XM_011520488 FLNC 2318 NM_001127487; NM_001458 UBE2C 11065 NM_001281741; NM_001281742; NM_007019; NM_181799; NM_181800; NM_181801 CTSK 1513 NM_000396 SULF2 55959 NM_001161841; NM_018837; NM_198596; NM_001387053; XM_006723830; NM_001387051; NM_001387055; NM_001387048; NM_001387049; NM_001387054; XM_011528914; NM_001387050; NM_001387052 TMEM45A 55076 XM_005247569; NM_018004; XM_024453614; XM_024453615; NM_001363876 FCGR1A   2209, 100132417 NM_000566 PLOD2 5352 NM_000935; NM_182943 C19orf80 55908 NM_018687 PDGFRL 5157 NM_006207 IGF2BP3 10643 NM_006547 SLC7A5 8140 NM_003486 PRRX1 5396 NM_006902; NM_022716 RARRES1 5918 NM_002888; NM_206963 LHFPL2 10184 NM_005779; XM_006714515 KDELR3 11015 NM_006855; NM_016657 TRIB3 57761 NM_001301201; XM_017027989; NM_001301188; NM_001301190; NM_001301193; NM_001301196; NM_021158 IL20RB 53833 NM_144717 FBLN1 2192 NM_001996; NM_006485; NM_006486; NM_006487 KMO 8564 NM_003679 C1R 715 NM_001733 CYP1B1 1545 NM_000104 KIF2A 3796 NM_001098511; NM_001243952; NM_004520 PLAUR 5329 NM_001301037; NM_001005376; NM_001005377; NM_002659 CKS2 1164 NM_001827 CDCP1 64866 NM_022842; NM_178181 SFRP4 6424 NM_003014 HAMP 57817 NM_021175 MMP9 4318 NM_004994 SLC3A1 6519 NM_000341; XM_011533047 NAT8 9027, 51471 NM_003960 FRMD3 257019 NM_001244959; NM_001244960; NM_001244961; NM_001244962; NM_174938 NPR3 4883 NM_000908; NM_001204375; NM_001204376 NAT8B 9027, 51471 NM_016347 BBOX1 8424 NM_003986; NM_001376258; NM_001376259; NM_001376260; NM_001376261; XM_011520402 SLC5A1 6523 NM_000343; NM_001256314 GBA3 57733 NM_020973; NM_001128432; NM_001277225 EMCN 51705 NM_016242; XM_011532024; NM_001159694 SLC47A1 55244 NM_018242 AQP1 358 NM_198098 PCK1 5105 NM_002591 UGT2A3 79799 NM_024743 BHMT 635 NM_001713 FMO1 2326 NM_001282693; NM_002021; NM_001282692; NM_001282694 ACAA2 10449, 648603 NM_006111 SLC5A8 160728 NM_145913 SLC16A9 220963 NM_001323981; NM_194298; XM_017015884 TSPAN18 90139 NM_130783; XM_006718373; XM_011520459 SLC17A3 10786 NM_006632; NM_001098486 STK32B 55351 NM_001306082; NM_018401 MAP7 9053 NM_001198609; NM_001198608; NM_001388350; NM_001198614; NM_001198617; NM_001388331; NM_001388333; NM_001388336; NM_001388340; NM_001388344; NM_001388345; NM_001388347; NM_001388348; NM_001388349; XM_011536246; NM_001198616; NM_001388330; NM_001388335; NM_003980; XM_011536243; NM_001388341; NM_001388342; NM_001388343; NM_001198611; NM_001388338; NM_001388346; NM_001198615; NM_001198618; NM_001198619; NM_001388329; NM_001388334; NM_001388339; NM_001388328; NM_001388332; NM_001388337; NM_001388351; NM_001388352; NM_001388353 MYLIP 29116 XM_005249033; NM_013262 SLC22A12 116085 NM_001276326; NM_001276327; NM_144585; NM_153378 LRP2 4036 NM_004525 CD34 947 NM_001025109; NM_001773 PODXL 5420 NM_001018111; NM_005397 ZBTB42 100128927 NM_001137601; NM_001370342 TEK 7010 NM_000459; NM_001290077; NM_001290078 FBP1 2203 NM_000507; NM_001127628 BCL2 596 NM_000633; NM_000657

In some embodiments, the determined characteristic of the biological sample may include tissue of origin for cells in the biological sample. In such embodiments, the set of genes used to obtain a gene ranking may include genes associated with biological features, expression pathways, or otherwise associated with determining tissue of origin. Some embodiments involve using a gene set for predicting tissue of origin for Diffuse Large B-Cell Lymphoma (DLBCL), such as germinal center B-cell (GCB) and activated B-cell (ABC). Examples of genes that may be included in such a gene set are listed in Table 3, below.

TABLE 3 Tissue of origin classifier for DLBCL NCBI Gene Gene ID NCBI Accession Number(s) ITPKB 3707 NM_002221; NM_001388404; XM_017001211 MYBL1 4603 NM_001080416; NM_001144755; NM_001294282 LMO2 4005 NM_001142315; NM_001142316; NM_005574; XM_005252921; XM_017017727; XM_017017728; XM_017017729; XM_017017730; XM_017017731; XM_017017732; XM_017017733 BATF 10538 NM_006399 IRF4 3662 NM_001195286; NM_002460 LRMP 4033 NM_001204126; NM_001204127; NM_006152; NM_001366543; NM_001366544; NM_001366546; NM_001366549; NM_001366545; NR_159367; NR_159368; NM_001366541; NR_159366; NM_001366540; NM_001366542; NM_001366547; NR_159369; NM_001366548 CCND2 894 NM_001759 SLA 6503 NM_001045556; NM_001045557; NM_001282964; NM_001282965; NM_006748; XM_017013739 SP140 11262 NM_001278452; NM_001005176; NM_001278451; NM_001278453; NM_007237 PIM1 5292 NM_002648; NM_001243186 CSTB 1476 NM_000100 BCL2 596 NM_000633; NM_000657 TCF4 6925 NM_003199; XM_017025956; NM_001348220; NM_001369581; NM_001369582; NM_001369585; XM_005266749; XM_005266761; XM_017025950; NM_001083962; NM_001243235; NM_001348211; NM_001348214; NM_001369583; XM_017025951; XM_024451241; NM_001369578; NM_001243227; NM_001243231; NM_001348218; NM_001369571; NM_001369575; NM_001369586; XM_005266755; XM_006722538; XM_017025954; NM_001243230; NM_001243233; NM_001306207; NM_001306208; NM_001330604; NM_001348212; XM_005266752; NM_001243236; NM_001330605; NM_001348213; NM_001348215; NM_001348217; NM_001348219; NM_001369577; NM_001369580; XM_017025952; XM_017025953; NM_001243226; NM_001348216; NM_001369567; NM_001369569; NM_001369573; NM_001369574; NM_001369579; NM_001243228; NM_001243232; NM_001243234; NM_001369568; NM_001369570; NM_001369572; NM_001369576; NM_001369584 P2RX5 5026 NM_001204519; NM_001204520; NM_002561; NM_175080 SPINK2 6691 NM_021114; NM_001271722; NM_001271720; NM_001271718; NM_001271721 VCL 7414 NM_003373; NM_014000 PTPN1 5770 NM_002827; NM_001278618 REL 5966 NM_001291746; NM_002908 FUT8 2530 NM_004480; NM_178155; NM_178156 RPL21 6144 NM_000982 PRKCB1 5579 NM_002738; NM_212535 CSNK1E 1454 NM_001289912; NM_001894; NM_152221 GPR18 2841 NM_001098200; NM_005292; XM_006719946 IGHM 3500, 3492, 28396, NG_001019.6 3502, 3507, 28450, 28452 ACP1 52 NM_004300; NM_007099 SPIB 6689 NM_003121; NM_001243998; NM_001243999; NM_001244000 HLA-DQA1 3117, 731682, NM_002122 100133678 KRT8 390601, 149501, 3856 NM_001256293; NM_002273 FAM3C 10447 NM_001040020; NM_014888; XM_011515736; XM_011515737 HLA-DMB 3109 NM_002118 GOT2 2806 NM_001286220; NM_002080 PIM2 11040 NM_006875 PLEK 5341 NM_002664

Some embodiments may involve determining a characteristic of a biological sample by using different gene sets and statistical models corresponding to the different gene sets to obtain characteristic predictions, which are used to determine the characteristic. FIG. 2 is a diagram of an illustrative processing pipeline 200 for determining a characteristic of a biological sample, which may include ranking genes based on their gene expression levels and using the rankings and statistical models to determine the characteristic, in accordance with some embodiments of the technology described herein. Processing pipeline 200 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, processing pipeline 200 may be performed by a desktop computer, a laptop computer, a mobile computing device. In some embodiments, processing pipeline 200 may be performed within one or more computing devices that are part of a cloud computing environment.

In some embodiments, gene expression data 102 is used to rank genes in different sets of genes based on their expression levels in gene expression data 102 to obtain multiple gene rankings. For example, a gene ranking may be obtained for each gene set and the gene ranking may be input to a statistical model trained using training data indicating rankings of expression levels for some or all genes in the gene set. As shown in FIG. 2 , ranking process 108 may involve using expression data 102 to rank genes in different gene sets, including Gene Set 1 106 a, Gene Set 2 106 b, Gene Set 3 106 c, and Gene Set 4 106 d, to obtain Gene Ranking 1 110 a, Gene Ranking 2 110 b, Gene Ranking 3 110 c, and Gene Ranking 4 110 d, respectively. Ranking process 108 may involve ranking genes in a set of genes based on numerical values of their expression levels. Different gene rankings may be obtained by ranking expression levels for different gene sets, and each gene ranking may be input to its respective statistical model to obtain a characteristic prediction. As shown in FIG. 2 , Gene Ranking 1 110 a, Gene Ranking 2 110 b, Gene Ranking 3 110 c, and Gene Ranking 4 110 d is provided as input to Statistical Model 1 112 a, Statistical Model 2 112 b, Statistical Model 3 112 c, and Statistical Model 4 112 d, respectively.

In some embodiments, the different statistical models and their respective gene sets may correspond to a particular characteristic of the biological sample. In such embodiments, each of the statistical models may output a prediction of the biological sample having a particular characteristic. In some instances, the prediction output by a statistical model may include a probability of the biological sample having the characteristic.

As shown in FIG. 2 , Statistical Model 1 112 a outputs Characteristic Prediction 1 116 a, Statistical Model 2 112 b outputs Characteristic Prediction 2 116 b, Statistical Model 3 112 c outputs Characteristic Prediction 3 116 c, and Characteristic Prediction 4 116 d outputs Characteristic Prediction 4 116 d. The predictions output by the different statistical models may be analyzed using prediction analysis process 118 to determine characteristic 114 for the biological sample. Prediction analysis process 118 may involve aggregating the different predictions and selecting a particular characteristic for the biological sample from among the different characteristic predictions. In some embodiments, a characteristic prediction may include a probability that the biological sample has the particular characteristic. In such embodiments, prediction analysis process 118 may involve aggregating the probabilities for the different characteristic predictions and selecting a characteristic based on the probabilities. In some embodiments, selecting the characteristic may involve selecting the characteristic having the highest probability as being characteristic 114.

Although four gene sets and four statistical models are shown in FIG. 2 , it should be appreciated that any suitable number of gene sets and corresponding statistical models may be implemented using the techniques described above in determining characteristic predictions and aggregating the characteristic predictions to obtain a characteristic of a biological sample. In some embodiments, the number of gene sets and corresponding statistical models may be in the range of 3 to 100, 3 to 70, 3 to 50, 3 to 40, 3 to 30, 5 to 50, 10 to 60, or 10 to 70.

In some embodiments, the number of gene sets and corresponding statistical models is equal to or less than the number of classes for the characteristic being predicted using processing pipeline 200. For instance, in embodiments where the characteristic being predicted is tissue of origin, the number of classes may correspond to the different types of tissue that can be determined using processing pipeline 200. Such embodiments may involve a different gene set and corresponding statistical model for each type of tissue. For example, Gene Set 1 106 a and Statistical Model 1 112 a may be used for generating a prediction of the biological sample being lung tissue (as Characteristic Prediction 1 116 a), Gene Set 2 106 b and Statistical Model 2 112 b may be used for generating a prediction of the biological sample being stomach tissue (as Characteristic Prediction 2 116 b), Gene Set 3 106 c and Statistical Model 3 112 c may be used for generating a prediction of the biological sample being liver tissue (as Characteristic Prediction 3 116 c), and Gene Set 4 106 d and Statistical Model 4 112 d may be used for generating a prediction of the biological sample being bladder tissue (as Characteristic Prediction 4). It should be appreciated that additional gene sets and their corresponding statistical models may be implemented for different tissue types. In some embodiments, there may be 21 gene sets and corresponding statistical models, allowing processing pipeline 200 to predict 21 types of tissue.

FIG. 3 is a flow chart of an illustrative process 300 for determining one or more characteristics of a biological sample using a gene ranking and a statistical model, in accordance with some embodiments of the technology described herein. Process 300 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 300 to determine one or more characteristics, such as characteristic(s) 114.

Process 300 begins at act 310, where expression data for a biological sample of a subject is obtained. In some embodiments, the expression data may be obtained using a gene expression microarray. In some embodiments, the expression data may be obtained by performing next generation sequencing. Some embodiments involve performing a sequencing process of the biological sample (e.g., a gene expression microarray, next generation sequencing) prior to obtaining expression data 102. In some embodiments, obtaining gene expression data 102 may involve obtaining gene expression data 102 in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way. In some embodiments, obtaining gene expression data 102 may involve analyzing a biological sample (in vitro) and accessing (e.g., by a computing device, a processor) the expression data. Further aspects relating to obtaining expression data are provided in the section titled “Obtaining Expression Data”.

Next, process 300 proceeds to act 320, where genes in a set of genes are ranked based on their expression levels in the expression data to obtain a gene ranking, such as by using ranking process 108. The expression data may include values, each representing an expression level for a gene in the set of genes, and determining the gene ranking may involve determining a relative rank for each gene in the set of genes based on the values.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. The set of genes may be selected from the group of genes listed in Table 1. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes may include all the genes listed in Table 1. In some embodiments, the set of genes may include 3-100 genes, 5-100 genes, 20-100 genes, 50-100 genes, 80-100 genes listed in Table 1. In some embodiments, the set of genes may include 100 or fewer genes, 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 1.

In some embodiments, the subject has, is suspected of having, or is at risk of having clear cell kidney cancer. The set of genes may be selected from the group of genes listed in Table 2. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes may include all the genes listed in Table 2. In some embodiments, the set of genes may include 3-80 genes, 5-80 genes, 20-80 genes, 50-80 genes, 70-80 genes listed in Table 2. In some embodiments, the set of genes may include 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 2.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. The set of genes may be selected from the group of genes listed in Table 3. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 3. In some embodiments, the set of genes may include all the genes listed in Table 3. In some embodiments, the set of genes may include 3-25 genes, 5-25 genes, 10-25 genes, 20-25 genes listed in Table 3. In some embodiments, the set of genes may include 25 or fewer genes, 20 or fewer genes, 15 or fewer genes, 10 or fewer genes listed in Table 3.

Next process 300 proceeds to act 320, where one or more characteristics of the biological sample is determined using the gene ranking and a statistical model, such as statistical model 112. In some embodiments, a characteristic determined by process 300 may include cancer grade for cells in the biological sample. In some embodiments, a characteristic determined by process 300 may include tissue of origin for cells in the biological sample. The statistical model may be trained using rankings of expression levels for one or more genes in the set of genes. In some embodiments, the gene ranking may be used as an input to the statistical model to obtain an output indicating the one or more characteristics. In some embodiments, the statistical model comprises a classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

In some embodiments, process 300 may include ranking genes in a second set of genes based on their expression levels in the expression data to obtain a second gene ranking. The second gene ranking and a second statistical model may be used to determine one or more second characteristics of the biological sample. The second statistical model may be trained using second training data indicating rankings of expression levels for some or all of the genes in the second set of genes. The one or more second characteristics of the biological sample may be different than a characteristic determined by act 330. For example, in some embodiments, a characteristic determined by act 330 may include cancer grade for cells in the biological sample and the second characteristic may include tissue of origin for cells in the biological sample.

In some embodiments, process 300 may include outputting the one or more characteristics to a user (e.g., physician), such as by displaying the one or more characteristics to the user on a graphical user interface (GUI), including the one or more characteristics in a report, sending an email to the user, and in any other suitable way.

In some embodiments, process 300 may include administering a treatment to the subject based on the determined one or more characteristics of the biological sample. For example, in embodiments where tissue of origin is determined for cells in the biological sample, a physician may administer a treatment for the subject associated with treating cancers of the determined tissue of origin. As yet another example, in embodiments where cancer grade is determined for cells in the biological sample, a physician may administer a treatment for the subject associated with treating cancers having the determined cancer grade. Further examples where characteristics of a biological sample determined using the techniques described herein are used for administering a treatment are provided in the section titled “Methods of Treatment”.

In some embodiments, process 300 may include identifying a treatment for the subject based on the determined characteristic(s) of the biological sample. For example, in embodiments where tissue of origin is determined for cells in the biological sample, the determined tissue of origin may be used to identify a treatment for the subject associated with treating cancers of the determined tissue of origin. As yet another example, in embodiments where cancer grade is determined for cells in the biological sample, the determined cancer grade may be used to identify a treatment for the subject associated with treating cancers having the determined cancer grade.

In some embodiments, process 300 may include determining a prognosis for the subject based on the determined one or more characteristics of the biological sample. For example, in embodiments where tissue of origin is determined for cells in the biological sample, the determined tissue of origin may be used to determine a prognosis for the subject associated with treating cancers of the determined tissue of origin. As yet another example, in embodiments where cancer grade is determined for cells in the biological sample, the determined cancer grade may be used to determine a prognosis for the subject associated with treating cancers having the determined cancer grade. Further aspects relating to other applications where characteristics of a biological sample determined using the techniques described herein are provided in the section titled “Applications”.

FIG. 4 is a flow chart of an illustrative process 400 for determining tissue of origin for cells in a biological sample, in accordance with some embodiments of the technology described herein. Process 400 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 400 to determine a tissue of origin.

Process 400 begins at act 410, where expression data for cells in a biological sample of a subject having, suspected of having, or is at risk of having cancer is obtained. In some embodiments, the expression data was obtained using a gene expression microarray. In some embodiments, the expression data was obtained by performing next generation sequencing. Some embodiments involve performing a sequencing process of the biological sample (e.g., a gene expression microarray, next generation sequencing) prior to obtaining expression data 102. In some embodiments, obtaining gene expression data 102 may involve obtaining gene expression data 102 in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way. In some embodiments, obtaining gene expression data 102 may involve analyzing a biological sample (in vitro) and accessing (e.g., by a computing device, processor) the expression data. Further aspects relating to obtaining expression data are provided in the section titled “Obtaining Expression Data”.

Next, process 400 proceeds to act 420, where genes in one or more sets of genes are ranked based on their expression levels in the expression data to obtain one or more gene rankings, such as by using ranking process 108. The expression data may include values, each representing an expression level for a gene in the one or more sets of genes, and determining a gene ranking may involve determining a relative rank for each gene in a set of genes based on the values.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. The set of genes may be selected from the group of genes listed in Table 1. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 1. The set of genes may consist of 5-100 genes, 10-200 genes, 20-100 genes, or 50-100 genes. In some embodiments, the set of genes may include all the genes listed in Table 1. In some embodiments, the set of genes may include 3-100 genes, 5-100 genes, 20-100 genes, 50-100 genes, 80-100 genes listed in Table 1. In some embodiments, the set of genes may include 100 or fewer genes, 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 1.

Next, process 400 proceeds to act 430, where tissue of origin for some or all of the cells in the biological sample is determined using the one or more gene rankings and one or more statistical models, such as statistical model 112. A statistical model may be trained using rankings of expression levels for some or all genes in a set of genes. Each of the gene rankings may be obtained based on respective expression levels for the one or more genes in the set of genes. In some embodiments, one or more gene rankings may be used as an input to the one or more statistical models to obtain an output indicating the tissue of origin. The tissue of origin may include lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue.

In some embodiments, the one or more statistical models comprises one or more classifiers selected from the group consisting of: a statistical model may include are a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

In some embodiments, process 400 may further include determining, using the gene ranking and the one or more statistical models, histological information (e.g., tissue type) for at least some of the cells in the biological sample. The histological information may include adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma. A combination of the tissue of origin and the histological information may be selected from the group consisting of lung adenocarcinoma, lung squamous cell carcinoma, melanoma, breast carcinoma, colorectal adenocarcinoma, ovarian serous cystadenocarcinoma, phenochromocytoma, bladder urothelial carcinoma, cervical squamous cell carcinoma, glioblastoma multiforme, head squamous cell carcinoma, neck squamous cell carcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, paraganglioma, prostate adenocarcinoma, sarcoma, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma.

FIG. 5 is a flow chart of an illustrative process 500 for determining a cancer grade for cells in a biological sample, in accordance with some embodiments of the technology described herein. Process 500 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 500 to determine a cancer grade.

Process 500 begins at act 510, where expression data for cells in a biological sample of a subject having, suspected of having, or is at risk of having cancer is obtained. In some embodiments, the expression data was obtained using a gene expression microarray. In some embodiments, the expression data was obtained by performing next generation sequencing. Some embodiments involve performing a sequencing process of the biological sample (e.g., a gene expression microarray, next generation sequencing) prior to obtaining expression data 102. In some embodiments, obtaining gene expression data 102 may involve obtaining gene expression data 102 in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way. In some embodiments, obtaining gene expression data 102 may involve analyzing a biological sample (in vitro) and accessing (e.g., by a computing device, processor) the expression data. Further aspects relating to obtaining expression data are provided in the section titled “Obtaining Expression Data”.

Next, process 500 proceeds to act 520, where genes in a set of genes are ranked based on their expression levels in the expression data to obtain a gene ranking, such as by using ranking process 108. The expression data may include values, each representing an expression level for a gene in the set of genes, and determining the gene ranking may involve determining a relative rank for each gene in the set of genes based on the values. The set of genes may consist of 5-500 genes, 5-200 genes, 50-500 genes, or 50-300 genes.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. The set of genes may be selected from the group of genes listed in Table 1. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 1. In some embodiments, the set of genes may include all the genes listed in Table 1. In some embodiments, the set of genes may include 3-100 genes, 5-100 genes, 20-100 genes, 50-100 genes, 80-100 genes listed in Table 1. In some embodiments, the set of genes may include 100 or fewer genes, 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 1.

In some embodiments, the subject has, is suspected of having, or is at risk of having clear cell kidney cancer. The set of genes may be selected from the group of genes listed in Table 2. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 2. In some embodiments, the set of genes may include 3-80 genes, 5-80 genes, 20-80 genes, 50-80 genes, 70-80 genes listed in Table 2. In some embodiments, the set of genes may include 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 2.

Next, process 500 proceeds to act 530, where cancer grade for the cells in the biological sample is determined using the gene ranking and a statistical model, such as statistical model 112. The statistical model may be trained using gene rankings of expression levels for one or more genes in the set of genes. Each of the gene rankings may be obtained based on respective expression levels for the one or more genes in the set of genes. In some embodiments, the gene ranking may be used as an input to the statistical model to obtain an output indicating the cancer grade. The cancer grade may include Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5. In some embodiments, the statistical model comprises a classifier selected from the group consisting of: a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

An example of how the techniques described herein may be implemented in predicting breast cancer grade are discussed in connection with FIGS. 6A, 6B, 6C, 6D, and 7 . FIG. 6A shows different data sets (data sets that vary in sample preparation, sequencing platform, data processing used to obtain expression data), associated clinical cancer grade for samples of the data sets, and predicted cancer grade obtained using the machine learning techniques described herein, for determining breast cancer grade. In particular, FIG. 6A shows different data sets (top panel) where each vertical line corresponds to a different sample, where the shading of the line corresponds to different data sets. FIG. 6A also shows the clinical grade associated with samples of the data sets, where the lighter shade indicates Grade 1 (“G1”) and the darker shade indicates Grade 3 (“G3”). The clinical grade may be a determination by a physician (e.g., pathologist) using microscopy to visually inspect the samples. FIG. 6B shows the enrichment signatures for different pathways, illustrating gene expression profiles associated with breast cancer Grade 1 and Grade 3. Genes in one or more of these pathways may be used for determining breast cancer grade according to the techniques described herein. As an example, the HALLMARK_G2M_CHECKPOINT signature is shown in the top panel and has a majority of upregulated genes for the right portion of samples and a majority of downregulated genes for the left portion of samples. Other examples of pathways associated with cancer grade classification for breast cancer are in Table 4, below. In particular the different pathways that are enriched in a set of genes that are upregulated for Grade 3 (“G3”) and pathways that are enriched in a set of genes that are upregulated for Grade 1 (“G1”) are listed in Table 4.

TABLE 4 Grade classifier for breast cancer gene set enrichment (according to MSigDB 6.1) Pathway enrichment Genes in/all Pathway P-value FDR genes Description Molecular G3 upregulated HALLMARK_G2M_CHECKPOINT 1.10E−60 1.40E−57 33/200 Genes defining early response to estrogen. HALLMARK_E2F_TARGETS 4.40E−47 6.10E−44 27/200 Genes encoding cell cycle related targets of E2F transcription factors. REACTOME_CELL_CYCLE_MITOTIC 3.80E−35 5.30E−32 24/325 Cell Cycle, Mitotic REACTOME_CELL_CYCLE 4.80E−34 6.60E−31 25/421 Cell Cycle HALLMARK_MITOTIC_SPINDLE 1.80E−28 2.40E−25 18/200 Genes important for mitotic spindle assembly. PID_PLK1_PATHWAY 6.20E−24 8.60E−21 11/46  PLK1 signaling events KEGG_CELL_CYCLE 4.30E−20 5.90E−17 12/128 Cell cycle PID_AURORA_B_PATHWAY 5.10E−20 7.00E−17 9/39 Aurora B signaling PID_FOXM1_PATHWAY 6.70E−20 9.30E−17 9/40 FOXM1 transcription factor network REACTOME_DNA_REPLICATION 1.70E−19 2.30E−16 13/192 DNA Replication REACTOME_MITOTIC_M_M_G1_PHASES 2.20E−18 3.10E−15 12/172 Genes involved in Mitotic M- M/G1 phases REACTOME_MITOTIC_PROMETAPHASE 3.00E−16 4.10E−13 9/87 Mitotic Prometaphase REACTOME_CELL_CYCLE_CHECKPOINTS 1.20E−14 1.60E−11  9/124 Cell Cycle Checkpoints HALLMARK_SPERMATOGENESIS 2.80E−14 3.80E−11  9/135 Genes up-regulated during production of male gametes (sperm), as in spermatogenesis. REACTOME_MITOTIC_G1_G1_S_PHASES 3.20E−14 4.40E−11  9/137 Mitotic G1-G1/S phases Molecular G1 upregulated NABA_MATRISOME 9.40E−09 1.30E−05  11/1028 Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins HALLMARK_ESTROGEN_RESPONSE_EARLY 9.50E−09 1.30E−05  6/200 Genes defining early response to estrogen. HALLMARK_TNFA_SIGNALING_VIA_NFKB 9.50E−09 1.30E−05  6/200 Genes regulated by NF-kB in response to TNF (Gene ID: 7124) NABA_CORE_MATRISOME 8.40E−08 0.00012  6/275 Ensemble of genes encoding core extracellular matrix including ECM glycoproteins, collagens and proteoglycans NABA_BASEMENT_MEMBRANES 2.50E−07 0.00034 3/40 Genes encoding structural components of basement membranes HALLMARK_ESTROGEN_RESPONSE_LATE 2.90E−07 0.0004  5/200 Genes defining late response to estrogen. KEGG_FOCAL_ADHESION 3.00E−07 0.00041  5/201 Focal adhesion PID_INTEGRIN4_PATHWAY 3.60E−07 0.0005 2/11 Alpha6 beta4 integrin-ligand interactions BIOCARTA_ACE2_PATHWAY 6.30E−07 0.00087 2/13 Angiotensin-converting enzyme 2 regulates heart function PID_INTEGRIN1_PATHWAY 1.90E−06 0.0026 3/66 Beta1 integrin cell surface interactions

FIGS. 12, 13, 14, 15, 16, and 17 illustrate relationships between biological features and different breast cancer grades. In particular, these figures describe the biology of molecular grades (Grade 1 and Grade 3) for breast cancer, where the data depicted is for TCGA BRCA, and the predicted breast cancer grades were obtained using the techniques described herein. FIG. 12 is a distribution of molecular cancer grade among PAM50 subtypes. FIG. 12 illustrates the majority of molecular Grade 1 samples belong to luminal subtypes. Further comparisons on breast cancer datasets for FIGS. 13-17 are for only luminal subtypes. FIG. 13 shows how progeny process scores correspond to given and predicted cancer grades in TCGA BRCA. The progeny process scores are calculated from expression data. FIG. 14 shows plots comparing different protein expression for different predicted cancer grades. The protein expression is according to RPPA data. FIG. 15 is a plot of cytolitic score (CYT) for different predicted cancer grades. FIG. 16 are plots showing the difference in mutations between different cancer grades. FIG. 16 illustrates genes, according to WES data, that are significantly differentially mutated between predicted cancer grades. FIG. 17 shows segments that are differentially amplified or deleted between predicted cancer grades. The segments shown in FIG. 17 are according to WES data.

To compare with the computational techniques described herein, FIG. 6A shows the predicted grade (lower panel) using the expression data and a statistical model according to the techniques described herein. The predicted grade shows how the different samples are predicted as being Grade 1 (“G1”) for the left portion of samples and as being Grade 3 (“G3”) for the right portion of samples. This is further shown in the plot of “G3 probability” over the different samples below the bottom panel of FIG. 6A, where the probability of the Grade 3 is higher for the right portion of samples than the left portion of samples. FIGS. 6C and 6D show similar data as that shown in FIGS. 6A and 6B, respectively, except that the samples and pathway signatures are associated with predicting breast cancer as being Grade 1 or Grade 3 for Grade 2 samples. Here, FIGS. 6C and 6D show how the biological features associated with Grade 2 is similar to the biological features associated with Grade 1 and Grade 3.

FIG. 7 is a plot of true positive rate versus false positive rate for a number of biological samples (shown in the solid line). The plot shows that the predicted cancer grade using the techniques described herein have a high true positive rate while maintaining a low false positive rate.

As another example, pathways associated with cancer grade classification for kidney clear cell are in Table 5, below. In particular the different pathways that are enriched in a set of genes that are upregulated for Grade 4 (“G4”) and for Grade 1 (“G1”) are listed in Table 5.

TABLE 5 Grade classifier for kidney clear cell cancer gene set enrichment (according to MsigDB 6-1) Pathway enrichment Genes in/all Pathway P-value FDR genes Description Molecular G4 upregulated HALLMARK_KRAS_SIGNALING_UP 1.70E−14 2.40E−11 9/200 Genes up-regulated by KRAS activation. HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 1.00E−12 1.40E−09 8/200 Genes defining epithelial- mesenchymal transition, as in wound healing, fibrosis and metastasis. NABA_MATRISOME 3.20E−12 4.40E−09 13/1028 Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins NABA_ECM_REGULATORS 4.80E−12 6.60E−09 8/238 Genes encoding enzymes and their regulators involved in the remodeling of the extracellular matrix KEGG_COMPLEMENT_AND_COAGULATION_CASCADES 1.40E−10 1.90E−07 5/69  Complement and coagulation cascades HALLMARK_COAGULATION 1.70E−10 2.30E−07 6/138 Genes encoding components of blood coagulation system; also up-regulated in platelets. REACTOME_IMMUNE_SYSTEM 4.10E−09 5.70E−06 10/933  Genes involved in Immune System NABA_MATRISOME_ASSOCIATED 7.50E−09 1.00E−05 9/753 Ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors PID_AVB3_OPN_PATHWAY 3.80E−08 5.30E−05 3/31  Osteopontin-mediated events PID_FRA_PATHWAY 8.00E−08 0.00011 3/37  Validated transcriptional targets of AP1 family members Fra1 and Fra2 HALLMARK_COMPLEMENT 8.50E−08 0.00012 5/200 Genes encoding components of the complement system, which is part of the innate immune system. PID_AMB2_NEUTROPHILS_PATHWAY 1.20E−07 0.00017 3/41  amb2 Integrin signaling PID_UPA_UPAR_PATHWAY 1.30E−07 0.00019 3/42  Urokinase-type plasminogen activator (uPA) and uPAR- mediated signaling PID_FGF_PATHWAY 4.10E−07 0.00056 3/55  FGF signaling pathway BIOCARTA_CLASSIC_PATHWAY 4.40E−07 0.0006 2/14  Classical Complement Pathway REACTOME_INNATE_IMMUNE_SYSTEM 6.00E−07 0.00083 5/279 Innate Immune System PID_INTEGRIN5_PATHWAY 8.20E−07 0.0011 2/17  Beta5 beta6 beta7 and beta8 integrin cell surface interactions BIOCARTA_COMP_PATHWAY 1.20E−06 0.0016 2/19  Complement Pathway NABA_ECM_GLYCOPROTEINS 2.40E−06 0.0034 4/196 Genes encoding structural ECM glycoproteins HALLMARK_INTERFERON_GAMMA_RESPONSE 2.70E−06 0.0037 4/200 Genes up-regulated in response to IFNG [GeneID = 3458]. HALLMARK_G2M_CHECKPOINT 2.70E−06 0.0037 4/200 Genes involved in the G2/M checkpoint, as in progression through the cell division cycle. HALLMARK_GLYCOLYSIS 2.70E−06 0.0037 4/200 Genes encoding proteins involved in glycolysis and gluconeogenesis. HALLMARK_HYPOXIA 2.70E−06 0.0037 4/200 Genes up-regulated in response to low oxygen levels (hypoxia). HALLMARK_TNFA_SIGNALING_VIA_NFKB 2.70E−06 0.0037 4/200 Genes regulated by NF-kB in response to TNF [GeneID = 7124]. PID_TOLL_ENDOGENOUS_PATHWAY 2.70E−06 0.0038 2/25  Endogenous TLR signaling Molecular G1 upregulated REACTOME_TRANSMEMBRANE_TRANSPORT_OF_(—) 1.20E−08 1.70E−05 6/413 SMALL_MOLECULES REACTOME_SLC_MEDIATED_TRANSMEMBRANE_(—) 1.60E−08 2.20E−05 5/241 SLC-mediated transmembrane TRANSPORT transport REACTOME_TRANSPORT_OF_GLUCOSE_AND_OTHER_(—) 4.50E−07 0.00063 3/89  SUGARS_BILE_ SALTS_AND_ORGANIC_ACIDS_METAL_(—) IONS_AND_AMINE_COMPOUNDS KEGG_PROXIMAL_TUBULE_BICARBONATE_(—) 5.40E−07 0.00074 2/23  Proximal tubule bicarbonate RECLAMATION reclamation REACTOME_GLUCONEOGENESIS 1.80E−06 0.0025 2/34  Gluconeogenesis

FIGS. 18, 19, 20, 21, and 22 illustrate relationships between biological features and different kidney clear cell grades. In particular, these figures describe the biology of molecular grades (Grade 1 and Grade 4) for kidney renal clear cell cancer, where the data depicted is for TCGA KIRC, and the predicted breast cancer grades were obtained using the techniques described herein. FIG. 18 shows how progeny process scores correspond to given and predicted cancer grades in TCGA KIRC. The progeny process scores are calculated from expression data. FIG. 19 is a plot illustrating chromosomal instability (CIN) for different cancer grades. FIG. 20 are plots comparing different protein expression, according to RPPA data, for different predicted cancer grades. FIGS. 21 and 22 illustrate genes, according to WES data, that are differentially amplified or deleted between predicted cancer grades.

Some embodiments involve using the techniques described herein for determining cancer grade for lung adenocarcinoma. Examples of genes that may be included in a gene set for determining cancer grade for lung adenocarcinoma are listed in Table 6, below. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 6. In some embodiments, the set of genes may include all the genes listed in Table 6. In some embodiments, the set of genes may include 3-25 genes, 5-25 genes, 10-25 genes, 20-25 genes listed in Table 6. In some embodiments, the set of genes may include 25 or fewer genes, 20 or fewer genes, 15 or fewer genes, 10 or fewer genes listed in Table 6.

TABLE 6 Cancer Grade Classifier for Lung Adenocarcinoma NCBI Gene Gene ID NCBI Accession Number(s) AADAC 13 NM_001086 ALDOB 229 NM_000035 ANXA10 11199 NM_007193 ASPM 259266 NM_001206846; NM_018136 BTNL8 79908 NM_001040462; NM_001159707; NM_001159708; NM_001159709; NM_001159710; NM_024850 CEACAM8 1088 NM_001816; XM_017026195; NM_001816 CENPA 1058 NM_001042426; NM_001809 CHGB 1114 NM_001819 CHRNA9 55584 NM_017581 COL11A1 1301 NM_001190709; NM_001854; NM_080629; NM_080630 CRABP1 1381 NM_004378; NM_004378 F11 2160 NM_000128 GGTLC1 92086 NM_178311; NM_178312; XM_005260865; XM_017028126 HJURP 55355 NM_001282962; NM_001282963; NM_018410 IGF2BP3 10643 NM_006547 IHH 3549 NM_002181 KCNE2 9992 NM_172201 KIF14 9928 NM_001305792; NM_014875; XM_011510231; XM_011510232; XM_017003005 LRRC31 79782 NM_001277127; NM_001277128; NM_024727; XM_011513160 MYBL2 4605 NM_001278610; NM_002466 MYOZ1 58529 NM_021245 PCSK2 5126 NM_001201528; NM_001201529; NM_002594 PI15 51050 NM_001324403; NM_015886 SCTR 6344 NM_002980 SHH 6469 NM_000193 SLC22A3 6581 NM_021977 SLC7A5 8140 NM_003486 SPOCK1 6695 NM_004598 TM4SF4 7104 NM_004617 TRPM8 79054 NM_024080 YBX2 51087 NM_015982

The techniques described herein may be implemented in predicting cancer grade for lung adenocarcinoma and are discussed in connection with FIGS. 23A, 23B, and 23C. In particular, a cancer grade classifier for lung adenocarcinoma may distinguish between molecular grade 1 (mG1), a low grade, and molecular grade 3 (mG3), a high grade. Such a classifier may be developed by using samples from TCGA LUAD (from the National Cancer Institute) and CPTAC3 (from NCBI) lung adenocarcinoma expression data as training data. For the classifier discussed in connection with FIGS. 23A, 23B, and 23C 117 samples of TCGA LUAD were excluded from the training data set and included as validation data. An initial gene set was formed from differentially expressed genes between grade 1 and grade 3. A genomic grade index (DOI: 10.1093/jnci/djj052) based on the initial gene set was calculated and training data set samples were split into high and low cancer grade based on survival mode. Through selection of the gene set used for the classifier, the number of genes was reduced. For example, the classifier discussed in connection with FIGS. 23A, 23B, and 23C, the initial gene set included 321 genes and the gene set used in the classifier included 31 genes. Validation data sets included 117 samples from TCGA LUAD and Series GSE68465. After hyperparameter tuning, the classifier's performance on the validation data set reached a 0.89AUC score in distinguishing between grade 1 and grade 3. These results demonstrated the capability of lung molecular grades to be statistically significant in predicting survival.

FIG. 23A shows validation data sets, associated cancer grade reported for samples of the data sets, predicted cancer grade obtained using the machine learning techniques described herein, for determining lung adenocarcinoma cancer grade, and the enrichment signatures for different pathways, illustrating gene expression profiles associated with grade 1 and grade 3. The validation data sets shown in FIG. 23A vary in vary in sample preparation, sequencing platform, and data processing used to obtain expression data. FIG. 23A shows data sets (top panels) where each vertical line corresponds to a different sample, where the shading of the line corresponds to different data sets. The cancer grade associated with samples of the data sets is shown, where the lighter shade indicates grade 1 and the darker shade indicates grade 3. The cancer grade associated with the samples may be a determination by a physician (e.g., pathologist) using microscopy to visually inspect the samples. The probability of molecular grade 3 predicted using the cancer grade classifier is also shown. FIG. 23A also shows the enrichment signatures for different pathways, illustrating gene expression profiles associated with lung adenocarcinoma grade 1 and grade 3. Genes in one or more of these pathways may be used for determining lung adenocarcinoma grade according to the techniques described herein. As an example, the HALLMARK_G2M_CHECKPOINT signature is shown in the top panel and has a majority of upregulated genes for the right portion of samples and a majority of downregulated genes for the left portion of samples. FIG. 23B shows results of applying validation data sets to lung adenocarcinoma cancer grade classifier. FIG. 23C is a plot of true positive rate versus false positive rate for predicting cancer grade of different biological samples where the classifier had a 0.894 AUC score.

FIG. 8A is a flow chart of an illustrative process 800 for selecting a gene set, in accordance with some embodiments of the technology described herein. Process 800 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 800 to select a gene set, which may be implemented in determining one or more characteristics of a biological sample, such as a tissue of origin, a cancer grade, and a PTCL subtype.

Process 800 begins at act 810, where expression data is ranked to obtain a gene ranking for the genes represented by the expression levels in the expression data. Ranking process 108 may be used in ranking the expression data to obtain the gene ranking.

The expression data used in selecting a gene set may include available expression data obtained through research organizations, including the National Cancer Institute (NCI) (e.g., Gene Expression Omnibus (GEO)), National Center for Biotechnology Information (NCBI), and The Cancer Imaging Archive (TCIA). For example, a gene set used for predicting breast cancer grade may be obtained by using expression data from Series GSE2990 available through the NCI. As another example, a gene set used for determining cancer grade for kidney clear cell cancer may be obtained by using expression data from Series GSE40435. As another example, a gene set used for determining tissue of origin and histological information (e.g., tissue type) for cancer may be obtained by using expression data from The Cancer Genome Atlas Program (TCGAP). As another example, a gene set used for predicting PTCL subtype may be obtained using expression data listed in Table 9.

Next, process 800 proceeds to act 820, where the ranked expression data is input into a statistical model, such as statistical model 112. An output indicating one or more desired characteristics may be obtained as a result of inputting the ranked expression data into the statistical model. Process 800 may proceed to act 830, where a validation quality score is calculated based on the output obtained by inputting the ranked expression data into the statistical model of act 820. A validation quality score may be calculated using one or more suitable metrics, including negative log loss, AUC, F-score (micro, macro, weighted), accuracy, balanced accuracy, precision, and recall.

Next, process 800 proceeds to act 840, where importance value(s) for different genes included in the ranking are calculated. An example of an importance value is a Shapley Additive Explanations (SHAP) value, which is described in “A Unified Approach to Interpreting Model Predictions” by Scott M. Lundberg and Su-In Lee, which is incorporated by reference in its entirety. Example SHAP values are shown in Table 7 in connection with a cell of origin classifier for DLBCL.

Next, process 800 proceeds to act 850, where the N (e.g., 1, 2, 3, 4) least important genes are excluded based on the importance values. Next process 800 proceeds to act 860, where a gene set updated based on excluding the N least important genes. In some embodiments, at least the gene have the lowest importance value is removed from the gene set.

Process 800 may initialize with a larger number of genes (e.g., ˜3,000 genes) in the gene set and decrease the number of genes in the set through subsequent iterations. Process 800 may continue by repeating the acts with the gene set selected in act 860 of the prior iteration until a desired quality score is achieved (e.g., a quality score higher than a threshold value). In some instances, an initial gene set may be ranked at act 810 and narrowed by process 800 to achieve a limited gene set used for a classifier as described herein.

FIG. 8B is a flow chart of an illustrative process 800 for selecting a gene set, in accordance with some embodiments of the technology described herein. Process 900 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 900 to select a gene set, which may be implemented in determining one or more characteristics of a biological sample, such as a tissue of origin, a cancer grade, and a PTCL subtype.

Process 900 begins at act 910, where an initial gene set is selected. The initial gene set selected may include a set of genes selected from Table 1, Table 2, Table 3, Table 6, and Table 8. The number of initial genes may be at least 1,000 genes, at least 3,000 genes, or at least 5,000 genes.

Next, process 900 proceeds to act 810, as discussed above in connection with process 800. Next process 900 proceeds to act 920, where hyperparameters for the statistical model are selected and fit to the statistical model.

Next process proceeds to acts 840, 850, and 860 as discussed above in connection with process 800. As discussed in connection with process 800, the initial set of genes may decrease in number though subsequent iterations of these steps. As a result of these iterative steps, process 900 proceeds to act 925, where a minimum size of gene set is reached.

As part of these iterative steps, process 900 proceeds to act 930, where a cross validation score is calculated based on inputting the ranked expression data into the statistical model of act 820. A cross validation score may be calculated by performing k-fold cross validation.

Process 900 proceeds to act 940, where a gene set is selected based on the cross validation score calculated in act 930. In some embodiments, the gene set selected has the highest cross validation score from a group of gene sets.

Next process 900 proceeds to act 950, where expression data is ranked to obtain a gene ranking for the genes represented by the expression levels in the expression data. Ranking process 108 may be used in ranking the expression data to obtain the gene ranking.

Next process 900 proceeds to act 960, where hyperparameters for the statistical model are selected and fit to the statistical model for the gene set selected in act 940.

For example, FIG. 9A is a plot of quality score versus number of genes, which illustrates how decreasing the number of genes to 28 from 30 increases the quality score. FIG. 9B is an exemplary plot of F1 score versus number of genes used in ranking for ABC/GCB tissue of origin prediction, in accordance with some embodiments of the technology described herein.

Cell of Origin DLBCL Classifier

As discussed herein, some embodiments involve using the techniques described herein for determining cell of origin for DLBCL. In particular, a cell of origin DLBCL classifier may categorize samples as being either germinal center B-cell (GCB) and activated B-cell (ABC). Such a classifier may be developed by using samples from Series GSE117556, Leipzig Lymphoma data set (10.1186/s13073-019-0637-7), Series GSE31312, Series GSE10846, Series GSE87371, Series GSE11318, Series GSE32918, Series GSE23501, Lymphoma/Leukemia Molecular Profiling Project (LLMPP), and Series GSE93984 as training data. For each data set, samples were selected to have a balanced cell of origin ratio ABC:GCB ratio of 40:60 per data set. For example, this may involve selecting samples having cell of origin labeling followed by a round of random selection of samples to obtained a desired ABC:GCB ratio. An example cell of origin DLBCL classifier is discussed in connection with FIGS. 24A, 24B, 24C, 24D, and 24E. For this classifier, the training data set includes 1,968 samples.

Suitable data sets may be used to validate the trained cell of origin DLBCL classifier. Validation of a cell of origin DLBCL may involve using data from Series GSE34171 (GPL96+GPL97), Series GSE22898, Series GSE64555, Series GSE145043, Series GSE19246, and the National Cancer Institute Center for Cancer Research (NCICCR) “Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma” data set. Validation of the classifier described in connection with FIGS. 24A, 24B, 24C, 24D, and 24E involved using a validation data set of 928 samples.

A classifier may be further validated using data sets of unknown and unclassified samples. A cell of origin DLBCL classifier may be validated using data from Series GSE69051, Series GSE69049, E-TABM-346, Series GSE68895, Series GSE38202, Series GSE2195, International Cancer Genome Consortium Malignant Lymphoma—DE (ICGC_MALY_DE) data set, and National Cancer Institute Cancer Genome Characterization Initiative (NCICGCI) Non-Hodgkin Lymphoma data set. For the cell of origin DLBCL classifier discussed in connection with FIGS. 24A, 24B, 24C, 24D, and 24E, 1,169 unknown and unclassified samples were used in validation of the classifier.

The cell of origin classifier discussed in connection with FIGS. 24A, 24B, 24C, 24D, and 24E may involve identifying a gene set, such as by process 800 shown in FIG. 8 . In particular, an initial gene set may be identified from genes discussed in Wright G, et al., A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma, PNAS, 2003; 100:9991-9996 (doi: 10.1073/pnas.1732008100), which is incorporated by reference herein in its entirety. The initial gene set was curated down to 30 genes to be used in the classifier. After hyperparameter tuning, the classifier's performance on a validation data set reached 0.93 f1-score and 0.978 AUC score.

In this example classifier, binary classification was performed using a gradient booster decision tree classifier in LightGBM. Feature selection was performed by estimating feature importance in the model using SHAP package. Example SHAP importance values calculated for possible genes to include in a cell of origin classifier for DLBCL are shown in Table 7, below.

TABLE 7 Genes for DLBCL cell of origin classifier and SHAP importance values Cell of origin genes SHAP importance ITPKB 1.198 MYBL1 1.15 LMO2 0.93 IRF4 0.94 LRMP 0.71 CCND2 0.7 BATF 0.66 SP140 0.52 SPINK2 0.54 TCF4 0.41 CSTB 0.41 PIM1 0.32 VCL 0.3 GPR18 0.24 FUT8 0.22 BCL2 0.28 SLA 0.24 RPL21 0.2 P2RX5 0.11 REL 0.12 HLA-DQA1 0.13 CSNK1E 0.16 PTPN1 0.05 KRT8 0.15 IGHM 0.13 PRKCB1 0.11 GOT2 0.11 FAM3C 0.07 SPIB 0.09 ACP1 0.06 PIM2 0.04 PLEK 0.06

FIG. 24A shows validation data sets, associated cell of origin reported for samples of the data sets, predicted cell of origin obtained using the machine learning techniques described herein, for determining DLBCL subtype, and the enrichment signatures for ABC and GCB subtypes. FIG. 24B shows validation data sets, associated cell of origin reported for samples of the data sets, predicted cell of origin obtained using the machine learning techniques described herein, for determining DLBCL subtype, and the enrichment signatures for ABC and GCB subtypes. The validation data sets shown in FIGS. 24A and 24B vary in sample preparation, sequencing platform, and data processing used to obtain expression data. Both FIGS. 24A and 24B shows data sets (top panel) where each vertical line corresponds to a different sample, where the shading of the line corresponds to different data sets. The cell of origin associated with samples of the data sets is shown, where the lighter shade indicates GCB subtype and the darker shade indicates ABC subtype. The cell of origin associated with the samples may be a determination by a physician (e.g., pathologist) using microscopy to visually inspect the samples. The enrichment signatures for ABC signature and GCB signature are shown in FIGS. 24A and 24B. The ABC signature generally has a majority of upregulated genes on the right portion of samples and the GBC signature has a majority of upregulated genes for the left portion of samples. FIGS. 24C and 24D are plots of survival rates for different groups (ABC, GCB). FIG. 24E is a plot of true positive rate versus false positive rate for predicting DLBCL subtype of different biological samples where the classifier had a 0.978 AUC score.

Human Papillomavirus (HPV) Head and Neck Squamous Cell Carcinoma Classifier

Some embodiments involve using the techniques described herein for predicting HPV status (HPV-positive, HPV-negative). Such embodiments may involve determining a sample as having an HPV-positive status or HPV-negative status. In some embodiments, the HPV status may be determined for a subject having, suspected of having, or at risk of having head and neck squamous cell carcinoma. Examples of genes that may be included in a gene set for determining HPV status for head and neck squamous cell carcinoma are listed in Table 8, below. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 8. In some embodiments, the set of genes may include all the genes listed in Table 8. In some embodiments, the set of genes may include 3-130 genes, 5-130 genes, 20-130 genes, 50-130 genes, 80-130 genes listed in Table 8. In some embodiments, the set of genes may include 130 or fewer genes, 100 or fewer genes, 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 8.

TABLE 8 HPV Status Classifier for Head and Neck Squamous Cell Carcinoma NCBI Gene Gene ID NCBI Accession Number(s) APOBEC3B 9582 NM_001270411; NM_004900 ATAD2 29028 NM_014109; NM_014109 BIRC5 332 NM_001168; NM_001012271; NM_001012270; NM_001168 CCL20 6364 NM_001130046; NM_004591 CCND1 595 NM_053056; NM_053056 CDC45 8318 NM_001178010; NM_001178011; NM_003504; XM_011530417; XM_011530416; NR_161281; XM_017028966; ; XR_002958716; NM_001178010; XM_011530418; XM_017028967; XM_024452277; NM_001369291 CDC7 8317 NM_001134419; NM_001134420; NM_003503; XM_005271241 CDK1 983 NM_001320918; NM_001786; NM_033379; XM_005270303 CDKN2A 1029 NM_000077; NM_000077; NM_001195132; NM_058197; XM_005251343; XM_011517676; ; NM_058196; NM_058195; XM_011517675; NM_001363763; XR_929159 CDKN2C 1031 NM_001262; NM_078626; NM_001262 CDKN3 1018 NM_001258 CENPF 1063 NM_016343; XM_017000086 CENPN 55839 NM_001100624; NM_001100625; NM_001270473; NM_001270474; NM_018455; XM_006721236; XM_017023456 CXCL14 9547 NM_004887 DCN 1634 NM_133505; NM_001920; NM_133503; NM_133504; NM_133505; NM_133506; NM_133507; DHFR 1719 NM_000791; NM_001290357; NM_000791; NM_001290354 DKK3 27122 NM_001330220; XM_017017554; XM_017017555; NM_001018057; NM_001330220; NM_013253; NM_015881; XM_006718178 DLGAP5 9787 NM_001146015; NM_014750; XM_017021840 EPCAM 4072 NM_002354 FANCI 55215 NM_001113378; NM_018193; XM_011521756; NM_001113378; NM_001376910; NM_001376911; ; XM_011521764 FEN1 2237 NM_004111; NM_004111 GMNN 51053 NM_001251989; NM_001251990; NM_001251991; NM_015895; XM_005249159; NM_001251989 GPX3 2878 NM_001329790; NM_002084 ID4 3400 NM_001546 IGLC1 3537 NG_000002.1 IL18 3606 NM_001243211; XM_011542805; NM_001243211; NM_001562; ; NM_001386420 IL1R2 7850 NM_001261419; NM_004633; XM_006712734; XM_011511804 KIF18B 146909 NM_001264573; NM_001265577; NM_001264573 KIF20A 10112 NM_005733 KIF4A 24137 NM_012310 KLK13 26085 NM_001348177; NM_001348178; NM_001348177; NM_015596 KLK7 5650 NM_005046; NM_139277; NM_001207053; NM_001243126; NM_005046 KLK8 11202 NM_001281431; NM_001281431; NM_007196; NM_144505; NM_144506; NM_144507 KNTC1 9735 NM_014708; XM_006719706 KRT19 3880 NM_002276 LAMP3 27074 NM_014398 LMNB1 4001 NM_005573 MCM2 4171 NM_004526 MCM4 4173 NM_005914; NM_182746 MCM5 4174 NM_006739; NM_006739 ME1 4199 NM_002395; XM_011535836 MELK 9833 NM_001256685; NM_001256687; NM_001256688; NM_001256689; NM_001256690; NM_001256692; NM_001256693; NM_014791; XM_011518076; XM_011518077; XM_011518078; XM_011518079; XM_011518081; XM_011518082; XM_011518083; XM_011518084 MKI67 4288 NM_001145966; NM_002417 MLF1 4291 NM_022443; NM_001130156; NM_001130157; NM_001195432; NM_001195433; NM_001195434; NM_022443; NM_001369782; NM_001378848; NM_001378853; ; NM_001369784; NM_001369785; NM_001369781; NM_001378846; NM_001378855; NM_001378845; NM_001378847; NM_001378850; NM_001378852; NM_001369783; NM_001378851 MMP12 4321 NM_002426 MTHFD2 10797 NM_006636; XM_006711924 NDN 4692 NM_002487; NM_002487 NEFH 4744 NM_021076 NEK2 4751 NM_001204182; NM_001204182; NM_001204183; NM_002497; XM_005273147 NUP155 9631 NM_153485; NM_001278312; NM_004298; NM_153485 NUP210 23225 NM_024923 NUSAP1 51203 NM_001243142; NM_001243143; NM_001243144; NM_001301136; NM_016359; NM_018454; XM_005254430 PDGFD 80310 NM_025208; NM_033135 PLAGL1 5325 NM_001080951; NM_001080952; NM_001080953; NM_001080954; NM_001289042; NM_001289043; NM_001289044; NM_001289047; NM_001289048; NM_001289049; NM_001317157; NM_001317159; NM_006718; NM_001080955; NM_001289037; NM_001289038; NM_001289039; NM_001289040; NM_001080951; NM_001080956; NM_001289041; NM_001289045; NM_001289046; NM_001317156; NM_001317158; NM_001317160; NM_001317161; NM_001317162 PLOD2 5352 NM_000935; NM_182943 PPP1R3C 5507 NM_005398 PRIM1 5557 NM_000946 PRKDC 5591 NM_001081640; NM_006904 PSIP1 11168 NM_001128217; NM_001317898; NM_001317900; NM_021144; NM_033222 RAD51AP1 10635 NM_001130862; NM_006479 RASIP1 54922 NM_017805; NM_017805 RFC5 5985 NM_001130112; NM_001130113; NM_007370; NM_181578; XM_011538643; XM_011538645 RNASEH2A 10535 NM_006397 RPA2 6118 NM_001286076; NM_001297558; NM_002946 RPL39L 116832 NM_052969 RSRC1 51319 NM_001271834; NM_001271838; NM_016625 RYR1 6261 NM_000540; NM_001042723 SLC35G2 80723 NM_001097599; NM_001097600; NM_025246; XM_006713773; XM_011513214; XM_017007289; XM_017007290; XM_017007291 SMC2 10592 NM_001265602; NM_001042550; NM_001042551; NM_001265602; NM_006444; XM_006716933; XM_011518148; XM_011518149; XM_011518153; XM_017014206; XM_017014207; XM_017014208 SPARCL1 8404 NM_001128310; NM_001291976; NM_001291977; NM_004684 STMN1 3925 NM_001145454; NM_005563; NM_203399; NM_203401 SYCP2 10388 NM_014258; XM_011528489 SYNGR3 9143 NM_004209 TIMELESS 8914 NM_001330295; NM_003920 TMPO 7112 NM_001032283; NM_001032284; NM_001307975; NM_003276; NM_001032283 TPX2 22974 NM_012112; XM_011528697; XM_011528699; NM_012112 TRIP13 9319 NM_004237; XM_011514163 TYMS 7298 NM_001071; NM_001071 UCP2 7351 NM_003355; NM_003355 UPF3B 65109 NM_023010; NM_080632 USP1 7398 NM_001017415; NM_001017416; NM_003368 ZSCAN18 65982 NM_001145542; NM_001145543; NM_001145544; NM_023926; XM_005259174; XM_006723335; XM_011527238; XM_011527239; XM_017027169; XM_017027170; XM_017027171 APOBEC3B 9582 NM_001270411; NM_004900 ATAD2 29028 NM_014109; NM_014109 BIRC5 332 NM_001168; NM_001012271; NM_001012270; NM_001168 CCL20 6364 NM_001130046; NM_004591 CCND1 595 NM_053056; NM_053056 CDC45 8318 NM_001178010; NM_001178011; NM_003504; XM_011530417; XM_011530416; NR_161281; XM_017028966; ; XR_002958716; NM_001178010; XM_011530418; XM_017028967; XM_024452277; NM_001369291 CDC7 8317 NM_001134419; NM_001134420; NM_003503; XM_005271241 CDK1 983 NM_001320918; NM_001786; NM_033379; XM_005270303 CDKN2A 1029 NM_000077; NM_000077; NM_ 001195132; NM_058197; XM_005251343; XM_011517676; ; NM_058196; NM_058195; XM_011517675; NM_001363763; XR_929159 CDKN2C 1031 NM_001262; NM_078626; NM_001262 CDKN3 1018 NM_001258 CENPF 1063 NM_016343; XM_017000086 CENPN 55839 NM_001100624; NM_001100625; NM_001270473; NM_001270474; NM_018455; XM_006721236; XM_017023456 CXCL14 9547 NM_004887 DCN 1634 NM_133505; NM_001920; NM_133503; NM_133504; NM_133505; NM_133506; NM_133507; DHFR 1719 NM_000791; NM_001290357; NM_000791; NM_001290354 DKK3 27122 NM_001330220; XM_017017554; XM_017017555; NM_001018057; NM_001330220; NM_013253; NM_015881; XM_006718178 DLGAP5 9787 NM_001146015; NM_014750; XM_017021840 EPCAM 4072 NM_002354 FANCI 55215 NM_001113378; NM_018193; XM_011521756; NM_001113378; NM_001376910; NM_001376911; XM_011521764 FEN1 2237 NM_004111; NM_004111 GMNN 51053 NM_001251989; NM_001251990; NM_001251991; NM_015895; XM_005249159; NM_001251989 GPX3 2878 NM_001329790; NM_002084 ID4 3400 NM_001546 IGLC1 3537 NG_000002.1 IL18 3606 NM_001243211;XM_011542805; NM_001243211; NM_001562; NM_001386420 IL1R2 7850 NM_001261419; NM_004633; XM_006712734; XM_011511804 KIF18B 146909 NM_001264573; NM_001265577; NM_001264573 KIF20A 10112 NM_005733 KIF4A 24137 NM_012310 KLK13 26085 NM_001348177; NM_001348178; NM_001348177; NM_015596 KLK7 5650 NM_005046; NM_139277; NM_001207053; NM_001243126; NM_005046 KLK8 11202 NM_001281431; NM_001281431; NM_007196; NM_144505; NM_144506; NM_144507 KNTC1 9735 NM_014708; XM_006719706 KRT19 3880 NM_002276 LAMP3 27074 NM_014398 LMNB1 4001 NM_005573 MCM2 4171 NM_004526 MCM4 4173 NM_005914; NM_182746 MCM5 4174 NM_006739; NM_006739 ME1 4199 NM_002395; XM_011535836 MELK 9833 NM_001256685; NM_001256687; NM_001256688; NM_001256689; NM_001256690; NM_001256692; NM_001256693; NM_014791; XM_011518076; XM_011518077; XM_011518078; XM_011518079; XM_011518081; XM_011518082; XM_011518083; XM_011518084 MKI67 4288 NM_001145966; NM_002417 MLF1 4291 NM_022443; NM_001130156; NM_001130157; NM_001195432; NM_001195433; NM_001195434; NM_022443; NM_001369782; NM_001378848; NM_001378853; ; NM_001369784; NM_001369785; NM_001369781; NM_001378846; NM_001378855; NM_001378845; NM_001378847; NM_001378850; NM_001378852; NM_001369783; NM_001378851 MMP12 4321 NM_002426 MTHFD2 10797 NM_006636; XM_006711924 NDN 4692 NM_002487; NM_002487 NEFH 4744 NM_021076 NEK2 4751 NM_001204182; NM_001204182; NM_001204183; NM_002497; XM_005273147 NUP155 9631 NM_153485; NM_001278312; NM_004298; NM_153485 NUP210 23225 NM_024923 NUSAP1 51203 NM_001243142; NM_001243143; NM_001243144; NM_001301136; NM_016359; NM_018454; XM_005254430 PDGFD 80310 NM_025208; NM_033135 PLAGL1 5325 NM_001080951; NM_001080952; NM_001080953; NM_001080954; NM_001289042; NM_001289043; NM_001289044; NM_001289047; NM_001289048; NM_001289049; NM_001317157; NM_001317159; NM_006718; NM_001080955; NM_001289037; NM_001289038; NM_001289039; NM_001289040; NM_001080951; NM_001080956; NM_001289041; NM_001289045; NM_001289046; NM_001317156; NM_001317158; NM_001317160; NM_001317161; NM_001317162 PLOD2 5352 NM_000935; NM_182943

Such a classifier may be developed by using samples from Series GSE65858, Series GSE41613, E-TABM-302 (from EMBL-EBI), Series GSE25727, Series GSEE3292, Series GSE6791, Series GSE10300, TCGA HNSC (from The Cancer Imaging Archive (TCIA)) data sets as training data. For classifier discussed in connection with FIGS. 25A, 25B, 25C, 25D, 25E, and 25F, 60 samples of the TCGA HNSC data set were excluded from the training data and used in validation data sets. Validation data sets included the 60 samples from the TCGA HNSC data set and Series GSE40774. Series GSE74927 was used as an additional validation data set where different strains of HPV virus are represented, allowing for assessment of the classifier's performance across different HPV strains. A gene set for the classifier was identified from genes discussed in Chakravarthy et al., Human Papillomavirus Drives Tumor Development Throughout the Head and Neck: Improved Prognosis Is Associated With an Immune Response Largely Restricted to the Oropharynx, Journal of Clinical Oncology, 34, no. 34 (Dec. 1, 2016) 4132-4141 (DOI: 10.1200/JCO.2016.68.2955), which is incorporated by reference herein in its entirety. The initial gene set was curated down to 82 genes, such as by using process 800 shown in FIG. 8 . After hyperparameter tuning, the classifier's performance on the validation data set with the TCGA HNSC data set and Series GSE40774 reached a 0.975 AUC score and 0.9 f1 score. The classifier's performance on the validation data set with Series GSE74927 reached a 1.0 AUC score and 1.0 f1 score. It is noted that that classifier successfully recognized several HPV strains, including HPV16 strain, HPV18 strain, HPV33 strain, and HPV55.

FIG. 25A shows validation data sets, associated HPV status reported for samples of the data sets, predicted HPV status obtained using the machine learning techniques described herein, for determining HPV status, and the enrichment signatures for different pathways, illustrating gene expression profiles associated with HPV status. Both FIG. 25A shows data sets (top panel) where each vertical line corresponds to a different sample, where the shading of the line corresponds to different data sets. The HPV status associated with samples of the data sets is shown, where the lighter shade indicates negative HPV status and the darker shade indicates positive HPV status. The probability of the sample having a positive HPV status is shown in the middle panel of FIG. 25A. The enrichment signatures for different pathways, illustrating gene expression profiles associated with HPV status are shown in FIG. 25A (bottom panel). As an example, the HALLMARK_E2F_TARGETS signature is shown in FIG. 25A and has a majority of upregulated genes for the right portion of samples and a majority of downregulated genes for the left portion of samples. FIGS. 25B and 25C are plots of survival rates for different groups of HPV status (positive HPV and negative HPV). FIG. 25D is a plot of true positive rate versus false positive rate for predicting HPV status of different biological samples (from the TCGA HNSC data set and Series GSE40774 validation data) where the classifier had a 0.975 AUC score. FIG. 25E is a plot of true positive rate versus false positive rate for predicting HPV status of different biological samples (from the Series GSE74927 validation data) where the classifier had a 1.0 AUC score. FIG. 25F is a plot of illustrating the performance of the classifier for different HPV strains in the Series GSE74927 validation data.

Peripheral T-Cell Lymphoma (PTCL) Classifier

Aspects of the present application relate to techniques, developed by the inventors, for analyzing gene expression data to determine a subtype of peripheral T-cell lymphoma (PTCL) for a biological sample. These techniques involve ranking a set of genes based on gene expression levels and using the ranking and one or more statistical models to determine the PTCL subtype. The set of genes may be associated with biological features (e.g., cell morphology, cell migration, cell cycle), expression pathways, or otherwise associated with one or more subtypes of peripheral T-cell lymphoma (PTCL).

Peripheral T-cell lymphomas accounts for approximately 10% of all non-Hodgkin lymphomas. Peripheral T-cell lymphomas are a heterogeneous group of diseases, which includes more than 20 subtypes, the exact definition of which is limited to modern methods of laboratory diagnosis. Examples of PTCL subtypes include but are not limited to Peripheral T-Cell Lymphoma, Not Otherwise Specified (PTCL-NOS), anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), Natural killer/T-cell lymphoma (NKTCL), Sezary syndrome, adult T-cell leukemia/lymphoma (ATLL), enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, hepatosplenic gamma-delta T-cell lymphoma, T-cell lymphomas of Follicular T-cell (TFH) origin, T-cell lymphomas of the gastrointestinal tract (e.g., EATL, MEITL), cutaneous T-cell lymphomas, etc.

The most frequent subgroups among PTCL are adult T-cell leukemia/lymphoma (ATLL), angioimmunoblastic T-cell lymphoma (AITL), NK/T-cell lymphoma (NKTCL), anaplastic large cell lymphoma (ALCL), and cases belong to the Not Otherwise Specified (PTCL-NOS), which correspond to approximately to 35% of the total PTCL patients. Other PTCL subtypes are rare and mostly represented by extranodal tumors. It is anticipated that more effective annotation of the PTCL will eventually lead to the design and implementation of personalized treatments. As discussed herein, the inventors have recognized certain benefits from using the ranking of a set of genes in contrast to particular values for gene expression levels. In some embodiments, the technology described herein involves determining a subtype of peripheral T-cell lymphoma (PTCL) for a biological sample.

For example, in some embodiments, rankings of genes based on the gene expression levels (in a biological sample) as determined by a sequencing platform may be provided as input to a statistical model trained to predict PTCL subtype for the biological sample. The statistical model may include a multi-class classifier and have multiple outputs corresponding to different PTCL subtypes. As another example, in some embodiments, rankings of genes based on the gene expression levels (in a biological sample) as determined by a sequencing platform may be provided as input to multiple statistical models trained to predict different PTCL subtypes. For example, one statistical model may be trained to predict anaplastic large cell lymphoma (ALCL) for the biological sample and another statistical model may be trained to predict angioimmunoblastic T-cell lymphoma (AITL) for the biological sample. In such embodiments, the statistical models may be binary classifiers, each being trained for a different PTCL subtype, or regression type classifiers estimating a likelihood of a particular PTCL subtype.

Different PTCL subtype(s) may have different molecular signatures. In some embodiments, the set of genes being ranked depends on the particular PTCL subtype(s) of interest. In some embodiments, one set of genes may be used for determining a group of PTCL subtype(s) and another set of genes may be used for determining a different group of PTCL subtype(s). For example, one set of genes may be used for determining a group of PTCL subtype(s) that include anaplastic large cell lymphoma (ALCL), and another set of genes may angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL). Another set of genes may be used for determining a group of PTCL subtype(s) that include enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, and hepatosplenic gamma-delta T-cell lymphoma. As another example, one set of genes may be used for determining anaplastic large cell lymphoma (ALCL), and another set of genes may be used for determining natural killer/T-cell lymphoma (NKTCL).

Some embodiments described herein address all of the above-described issues that the inventors have recognized with determining PTCL subtype of a biological sample using gene expression data. However, not every embodiment described herein addresses every one of these issues, and some embodiments may not address any of them. As such, it should be appreciated that embodiments of the technology described herein are not limited to addressing all or any of the above-discussed issues with determining PTCL subtype of a biological sample using gene expression data.

Some embodiments involve obtaining gene expression data for a biological sample of a subject, ranking genes in set(s) of genes based on their expression levels in the expression data to obtain one or more gene rankings. The one or more gene rankings may be used, along with one or more statistical models, to determine a subtype of PTCL for cells in the biological sample. The statistical model may be trained using rankings of expression levels for some or all genes in the set(s) of genes.

In some embodiments, the gene ranking(s) may be obtained by ranking genes in one or more sets of genes based on their expression levels in the expression data. In some embodiments, the expression data includes values, each representing an expression level for a gene in the set(s) of genes. Determining the gene ranking(s) may involve determining a relative rank for each gene in the set(s) of genes based on the values. For example, a first gene ranking may be obtained by ranking genes in a first set of genes based on their expression levels.

In some embodiments, the expression data may be obtained for cells in the biological sample, where the subject has or is suspected of having cancer. In some embodiments, the expression data may be obtained for cells in the biological sample, where the subject has or is suspected of having lymphoma. In some embodiments, the subject has or is suspected of having PTCL.

In some embodiments, processing pipeline 100 shown in FIG. 1 may be used for determining one or more PTCL subtypes. In such embodiments, a gene ranking and a statistical model may be used to determine one or more PTCL subtypes of a biological sample. In some embodiments, one set of genes may be used for determining PTCL subtype for the biological sample and another set of genes may be used for determining tissue of origin. For example, statistical model 112 a and gene set 1 106 a may be used for determining PTCL subtype for cells in the biological sample and statistical model 112 b and gene set 2 106 b may be used for determining tissue of origin for cells in the biological sample. In some embodiments, different gene sets may be used for determining different PTCL subtypes. For example, gene set 1 106 a may be used for determining whether the biological sample has the AITL subtype and gene set 2 106 b may be used for determining whether the biological sample has the ATLL subtype.

In some embodiments, different gene sets and different statistical models may be used for determining different PTCL subtypes. For example, statistical model 112 a and gene set 1 106 a may be used for determining one PTCL subtype (e.g., AITL) for cells in the biological sample and statistical model 112 b and gene set 2 106 b may be used for another PTCL subtype (e.g., ATLL) for cells in the biological sample.

A statistical model used for determining PTCL subtype may be trained using data from one or more of Series GSE58445, Series GSE45712, Series GSE1906, Series GSE90597, Series GSE6338, Series GSE36172, Series GSE65823, Series GSE118238, Series GSE78513, Series GSE51521, Series GSE14317, Series GSE80631, Series GSE19067, and Series GSE20874 available through the GEO database. As another example, a statistical model used for determining PTCL subtype may be trained using data from one or more of the cohorts listed in Table 9, below.

TABLE 9 Cohorts of patients with gene expression data for training statistical model(s) for PTCL subtype classification. Cohort Database Platform Year GSE58445 GEO GPL570 2014 n = 191 GSE45712 GEO GPL8432 2018 n = 101 GPL14591 GSE19069 GEO GPL570 2015 n = 100 GSE90597 GEO GPL10739 2018 n = 66 GSE6338 GEO GPL570 2007 n = 40 GSE36172 GEO GPL6480 2013 n = 38 E-TABM-783 ArrayExpress GPL570 2009 n = 33 GSE65823 GEO GPL570 2015 n = 31 GSE118238 GEO GPL570 2018 n = 29 E-TABM-702 ArrayExpress GPL570 2014 n = 23 GSE78513 GEO GPL570 2016 n = 23 GSE51521 GEO GPL17811 2018 n = 20 GSE14317 GEO GPL571 2009 n= 19 GSE80631 GEO GPL6883 2016 n = 19 GSE19067 GEO GPL570 2010 n = 18 GSE20874 GEO GPL10175 2011 n= 18 SRP049695 SRA RNASeq n = 17 SRP029591 SRA RNASeq 2014 n= 10

In some embodiments, PTCL subtype may be determined using the techniques described herein for cells in a biological sample. PTCL subtype may include Peripheral T-Cell Lymphoma, Not Otherwise Specified (PTCL-NOS), anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), Natural killer/T-cell lymphoma (NKTCL), Sezary syndrome, adult T-cell leukemia/lymphoma (ATLL), enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, hepatosplenic gamma-delta T-cell lymphoma, T-cell lymphomas of Follicular T-cell (TFH) origin, T-cell lymphomas of the gastrointestinal tract, and cutaneous T-cell lymphomas.

In some embodiments, a set of genes used to obtain a gene ranking may include genes associated with biological features, expression pathways, or otherwise associated with determining one or more PTCL subtypes. Examples of genes that may be included in such a gene set are listed in Table 10, below.

TABLE 10 Gene Set for PTCL Subtype Classifier NCBI Gene Gene ID NCBI Accession Number(s) EFNB2 1948 NM_004093 ROBO1 6091 NM_133631; NM_001145845; NM_002941; XM_006713277; XM_017006983 S1PR3 1903 NM_005226 ANK2 287 NM_001127493; NM_001148; NM_020977 LPAR1 1902 NM_001401; NM_057159; NM_001351414; NM_001351415; NM_001387481; NM_001387505; XM_005251782; NM_001351401; NM_001387470; NM_001387480; NM_001387486; NM_001387498; NM_001387502; NM_001387503; NM_001387509; NM_001387511; NM_001387521; NM_001351398; NM_001351399; NM_001351400; NM_001351419; NM_001387478; NM_001387493; NM_001387497; NM_001387506; NM_001387507; NM_001387508; NM_001387520; NM_001351416; NM_001387476; NM_001387491; NM_001387471; NM_001387472; NM_001387477; NM_001387485; NM_001387492; NM_001387494; NM_001387510; NM_001387517; NM_001351397; NM_001351405; NM_001351406; NM_001351407; NM_001351413; NM_001351418; NM_001387475; NM_001387483; NM_001387484; NM_001387488; NM_001387490; NM_001387496; NM_001387516; NM_001387519; NM_001351404; NM_001351408; NM_001351410; NM_001351420; NM_001387473; NM_001387474; NM_001387487; NM_001387489; NM_001387495; NM_001387514; NM_001387518; NM_001351402; NM_001351403; NM_001351409; NM_001351411; NM_001351412; NM_001351417; NM_001387479; NM_001387482; NM_001387501; NM_001387504; NM_001387512; NM_001387513; NM_001387515 SNAP91 9892 XM_011536269; XM_017011557; XM_017011558; XM_017011559; XM_017011560; XM_005248770; XM_017011564; XM_017011571; NM_001376687; NM_001376688; NM_001376698; NM_001376700; NM_001376709; NM_001376715; NM_001376716; NM_001376723; NM_001376728; NM_001376731; NM_001376734; XM_011536266; XM_017011575; XM_017011586; NM_001376676; NM_001376682; NM_001376685; NM_001376702; NM_001376711; NM_001376736; NM_001376738; NR_164844; XM_006715615; XM_011536265; XM_017011570; XM_017011582; XM_024446600; NM_001376691; NM_001376699; NM_001376720; NM_001376735; NM_001376740; NM_014841; NR_164843; NR_164845; XM_011536275; XM_017011562; XM_017011565; XM_017011569; XM_017011580; NR_026669; NM_001256717; NM_001376677; NM_001376680; NM_001376683; NM_001376708; NM_001376718; NM_001376726; NM_001376737; XM_011536273; XM_011536276; XM_017011567; XM_017011583; XM_017011584; NM_001242794; NM_001376679; NM_001376694; NM_001376695; NM_001376697; NM_001376710; NM_001376714; NM_001376717; NM_001376742; XM_017011572; XM_017011577; XM_017011581; XM_017011590; NM_001256718; NM_001363677; NM_001376678; NM_001376686; NM_001376690; NM_001376693 SOX8 30812 NM_014587 RAMP3 10268 NM_005856 TUBB2B 347733 NM_178012 ARHGEF10 9639 NM_001308152; NM_001308153; NM_014629; XM_017014003 NOTCH1 4851 NM_017617 ZBTB17 7709 NM_001242884; NM_001287603; NM_003443; XM_011542088 CCNE1 898 NM_001238; NM_001322262; NM_001322259; NM_001322261 FGF18 8817 NM_003862 MYCN 4613 NM_001293231; NM_001293228; NM_001293233; NM_005378 PTHLH 5744 NM_198965; NM_198966; XM_011520774; NM_002820; XM_017019675; NM_198964 SMARCA2 6595 NM_001289400; NM_001289399; NM_001289398; NM_001289397; NM_001289396; NM_003070; NM_139045 WNK1 65125 NM_014823; NM_018979; NM_001184985; NM_213655; XM_017019837; XM_017019838 NKX2-1 7080 NM_001079668; NM_003317 CYP26A1 1592 NM_000783; NM_057157 HPSE 10855 NM_001098540; NM_001166498; NM_001199830; NM_006665 CTLA4 1493 NM_001037631; NM_005214 PELI1 57162 NM_020651; XM_011532994; XM_017004520 PRKCB 5579 NM_002738; NM_212535 SPAST 6683 NM_014946; NM_199436 ALS2 57679 NM_001135745; NM_020919; XM_006712654 KIF3B 9371 NM_004798 ZFYVE27 118813 XM_005269509; XM_011539254; XR_945597; NM_001002262; NM_001174120; NM_001385878; NM_001385889; NM_001385895; NM_001385901; NM_001385918; NR_169800; XM_005269508; XR_945594; NM_001385877; NM_001385902; NM_001385903; NM_001385904; NR_169794; NR_169795; NR_169797; NR_169803; NR_169805; NR_169809; XM_011539253; XM_017015644; XR_002956957; NM_001174121; NM_001174122; NM_001385879; NM_001385881; NM_001385883; NM_001385900; XM_017015645; NM_001385875; NM_001385886; NM_001385896; NR_169796; XM_011539252; NM_001385876; NM_001385880; NM_001385887; NM_001385894; NM_001385898; NM_001385915; NM_001385919; XM_017015646; NM_001385882; NM_001385884; NM_001385890; NM_001385897; NM_001385899; NR_169804; NR_169810; NM_001002261; NM_001385871; NM_001385892; NM_001385905; NM_144588; NR_169802; NR_169806; NR_169808; NR_169811; XR_002956956; NM_001174119; NM_001385885; NM_001385888; NM_001385891; NM_001385893; NM_001385906; NM_001385908; NM_001385911; NM_001385916; NR_169798; NR_169799; NR_169801 FGF18 8817 NM_003862 FNTB 2342 NM_001202558; NM_002028 REL 5966 NM_001291746; NM_002908 DMRT1 1761 NM_021951 SLC19A2 10560 NM_001319667; NM_006996 STK3 6788 NM_001256313; NM_001256312; NM_006281 PERP 64065 NM_022121 TNFRSF8 943 NM_001243; NM_001281430 TMOD1 7111 NM_001166116; NM_003275 BATF3 55509 NM_018664 CDC14B 8555 NM_001077181; NM_003671; NM_033331; XM_011519153; XM_017015240; XM_017015247; XR_001746407; XR_001746409; NM_001351567; XM_017015248; XM_017015249; XR_929865; XM_011519147; XM_017015242; XM_017015244; XM_017015245; XR_929864; NM_001351568; XM_011519149; XM_011519152; XR_001746408; NM_001351570; NM_033332; XM_011519148; XM_011519151; XR_929868; NR_147239; XM_011519156; XR_002956814; XM_011519159; XM_017015241; XR_001746406; NM_001351569 WDFEY3 N/A N/A AGT 183 NM_000029 ALK 238 NM_004304 ANXA3 306 NM_005139 BTBD11 121551 NM_001017523; NM_001018072; NM_001347943 CCNA1 8900 NM_001111045; NM_001111046; NM_001111047; NM_003914; XM_011535294; XM_011535295; XM_011535296 DNER 92737 NM_139072 GAS1 2619 NM_002048 HS6ST2 90161 NM_001077188; NM_147175; XM_011531407; XM_017029945; XM_011531408; XM_017029946; XM_005262491; XM_011531406 IL1RAP 3556 NM_001167928; NM_001167929; NM_001167930; NM_001167931; NM_002182; NM_134470 PCOLCE2 26577 NM_013363 PDE4DIP 9659 XM_011510175; NM_001195261; NM_001002810; NM_001002811; NM_001002812; NM_001195260; NM_001198832; NM_001198834; NM_014644; NM_022359 SLC16A3 9123 NM_001042422; NM_001042423; NM_001206950; NM_001206951; NM_001206952; NM_004207; XM_024451023 TIAM2 26230 NM_001010927; NM_012454 TUBB6 84617 NM_001303527; NM_001303525; NM_001303524; NM_001303526; NM_001303528; NM_001303529; NM_001303530; NM_032525 WNT7B 7477 XM_011530366; NM_058238 SMOX 54498 NM_001270691; NM_175839; NM_175840; NM_175841; NM_175842; XM_011529261 TMEM158 25907 NM_015444 NLRP7 199713 NM_001127255; NM_139176; NM_206828; XM_006723075; XM_006723076; XM_011526599 ADRB2 154 NM_000024 GALNT2 2590 NM_004481; NM_001291866 HRASLS 57110 NM_020386; NM_001366112; XM_011513034; XM_011513035 CD244 51744 NM_001166663; NM_001166664; NM_016382; XM_011509622 FASLG 356 NM_000639; NM_001302746 KIR2DL4 3805 NM_001080772; NM_001080770; NM_002255 LOC100287534 100287534 HF584483.1 KLRD1 3824 NM_007334; XM_006719067; XR_001748697; XM_017019289; NM_001351062; NR_147038; XM_017019287; NM_001351063; XM_011520650; XM_017019286; XM_024448974; XR_001748696; NR_147040; XM_017019285; NM_001114396; NM_001351060; NR_147039; XM_011520651; XM_017019288; NM_002262 SH2D1B 117157 NM_053282 KLRC2 3822 NM_002260 NCAM1 4684 NM_001242607; NM_000615; NM_001076682; NM_001242608; NM_181351 CXCR5 643 NM_001716; NM_032966 IL6 3569 NM_001318095; NM_000600; XM_011515390 ICOS 29851 NM_012092 CD40LG 959 NM_000074 CD84 8832 NM_001184879; NM_001184881; NM_001184882; NM_001330742; NM_003874 IL21 59067 NM_021803; NM_001207006 BCL6 604 NM_001134738; NM_001130845; NM_001706; XM_005247694; XM_011513062 MAF 4094 XM_017023233; XM_017023234; XM_017023235; NM_001031804; NM_005360 SH2D1A 4068 NM_001114937; NM_002351 IL4 3565 NM_000589; NM_172348 PTPN1 5770 NM_002827; NM_001278618 PIM1 5292 NM_002648; NM_001243186 ENTPD1 953 NM_001098175; NM_001164178; NM_001164181; NM_001164182; NM_001164183; NM_001312654; NM_001320916; NM_001776; XM_011540371; XM_011540377; XM_017016959 IRF4 3662 NM_001195286; NM_002460 CCND2 894 NM_001759 IL16 3603 NM_001172128; NM_004513; NM_172217; NR_148035; NM_001352685; NM_001352686; NM_001352684 ETV6 2120 NM_001987 BLNK 29760 NM_001258441; NM_001258442; NM_001114094; NM_001258440; NM_013314 SH3BP5 9467 NM_001018009; XM_017007522; XM_017007523; XM_017007524; XM_017007525; NM_004844 FUT8 2530 NM_004480; NM_178155; NM_178156 CCR4 1233 NM_005508; XM_017005687 GATA3 2625 NM_001002295; NM_002051; XM_005252442; XM_005252443 IL5 3567 NM_000879; XM_011543373; XM_011543374 IL10 3586 NM_000572 IL13 3596 NM_002188 MMEITPKB N/A N/A MYBL1 4603 NM_001080416; NM_001144755; NM_001294282 LRMP 16970 NM_001204126; NM_001204127; NM_006152; NM_001366543; NM_001366544; NM_001366546; NM_001366549; NM_001366545; NR_159367; NR_159368; NM_001366541; NR_159366; NM_001366540; NM_001366542; NM_001366547; NR_159369; NM_001366548 KIAA0870 22898 NM_014957 LMO2 4005 NM_001142315; NM_001142316; NM_005574; XM_005252921; XM_017017727; XM_017017728; XM_017017729; XM_017017730; XM_017017731; XM_017017732; XM_017017733 CR1 1378 NM_000651; NM_000573 LTBR 4055 NM_001270987; NM_002342 PDPN 10630 NM_001006624; NM_001006625; NM_006474; NM_198389; XM_006710295 TNFRSF1A 7132 NM_001346091; NM_001065; NM_001346092 FCER2 2208 NM_001207019; NM_001220500; NM_002002; XM_005272462 ICAM1 3383 NM_000201 FCGR2B 2213 NM_001002273; NM_001002274; NM_001002275; NM_001190828; NM_004001; XM_024454043; NM_001386004; NR_169827; NM_001386001; NM_001386002; NM_001386006; NM_001386003; XM_017000670; NM_001386000; NM_001386005; XM_024454047 IKZF2 22807 NM_001079526; NM_016260; XM_005246384; XM_005246385; XM_011510818; NM_001371277; XM_011510809; NM_001387220; XM_005246386; XM_011510810; XM_011510803; XM_011510804; XM_011510812; XM_011510815; XM_011510817; XM_017003592; NM_001371275; XM_011510808; NM_001371274; XM_011510802; XM_011510807; XM_011510819; NM_001371276; XM_011510805; XM_011510811; XM_017003591; XM_011510816 CCR8 1237 NM_005201 TNFRSF18 8784 XM_017002722; NM_004195; NM_148901; NM_148902 IKZF4 64375 NM_022465; XM_005269089; XM_017019813; XM_017019815; XM_024449128; XM_024449129; NM_001351090; XM_017019807; XM_017019812; XM_024449131; NM_001351089; XM_011538664; XM_011538669; XM_017019814; XM_017019808; XM_024449130; NM_001351092; XM_017019806; XM_017019809; XM_017019810; XM_005269086; XM_017019811; XM_017019816; NM_001351091 FOXP3 50943 XM_006724533; NM_001114377; NM_014009 IL2 3558 NM_000586 TBX21 30009 NM_013351 IFNG 3458 NM_000619 GZMH 2999 NM_001270781; NM_033423 GNLY 10578 NM_001302758; NM_006433; NM_012483 EOMES 8320 NM_001278182; NM_001278183; NM_005442 NCR1 9437 NM_004829; NM_001145458; NM_001145457; NM_001242356; NM_001242357 GZMB 3002 NM_001346011; NM_004131 NKG7 4818 NM_005601 FGFBP2 83888 NM_031950 KLRF1 51348 NM_001291822; NM_001291823; NM_016523 CD160 11126 NM_007053; XM_005272929; XM_011509104 KLRK1 22914 NM_001199805; NM_007360 CD226 10666 NM_001303619; XM_005266643; XM_017025525; NM_006566; XM_006722374; XM_017025526; NM_001303618; XM_005266642; XM_017025527 NCR3 259197 NM_001145466; NM_001145467; NM_147130; XM_006715049; XM_011514459 TNFRSF8 943 NM_001243; NM_001281430 BATF3 55509 NM_018664 TMOD1 7111 NM_001166116; NM_003275 TMEM158 25907 NM_015444 MSC 9242 NM_005098 POPDC3 64208 NM_022361; XM_011536067; XM_017011194; XM_017011195

Some embodiments involve using a gene set that includes genes associated with a molecular signature of one or more PTCL subtypes. Examples of genes that may be included in such a gene set are listed in Table 11, below, which shows different genes and their corresponding PTCL subtype. In some embodiments, one or more genes listed in Table 11 may be combined with one or more genes listed in Table 10 to form a gene set used for determining PTCL subtype according to the techniques described herein.

TABLE 11 Functional annotation of the representative genes in the molecular signatures of the common PTCL subtypes. PTCL subgroups Major functional category Gene symbols AITL Cell morphology/Intracellular EFNB2, ROBO1, S1PR3, ANK2, LPAR1, signaling SNAP91,SOX8 Cell migration/Vascularization LPAR1, RAMP3, S1PR3, ROBO1, EFNB2, TUBB2B, SOX8 Cell cycle SOX8, ARHGEF10 ATLL T-cell NOTCH1, ZBTB17, CCNE1, FGF18, MYCN, homeostasis/activation/ PTHLH, SMARCA2, WNK1, NKX2-1, CYP26A1, differentiation HPSE, CTLA4, MYCN, PELI1, PRKCB, SPAST, ALS2, KIF3B, ZFYVE27 Cell cycle/Proliferation FGF18, MYCN, NKX2-1, NOTCH1, PTHLH, SMARCA2, CCNE1, GF18, WNK1, CTLA4, PELI1, PRKCB, ZBTB17, HPSE, FNTB, REL-1 ALCL Cell morphology/Intracellular DMRT1, SLC19A21, STK3, PERP, TNFRSF8, signaling TMOD1, BATF3, DNER, ADRB2, AGT, TIAM2, and interaction HS6ST2, GAS1, IL1RAP, WNT7B, ARHGEF10, HRASLS P53-induced genes CDC14B, PERP, WDFEY3, TMOD1 Cell cycle/Proliferation AGT, ALK, ANXA3, BTBD11, CCNA1, DNER, GAS1, HS6ST2, IL1RAP, PCOLCE2, PDE4DIP, SLC16A3, TIAM2, TUBB6, WNT7B, SMOX, TMEM158, NLRP7, ADRB2, GALNT2 NKTCL NK-cell activation/survival CD244, FASLG, KIR2DL4, KLRD1, SH2D1B NK cell markers CD244, FASLG, KLRC2, KLRD1, NCAM1

Further examples of genes that may be included in a gene set used for determining PTCL subtype according to the techniques described herein are described and listed in Iqbal J, Wright G, Wang C, et al., Gene expression signatures delineate biological and prognostic subgroups in peripheral T-cell lymphoma, Blood, 2014; 123(19):2915-2923 (doi:10.1182/blood-2013-11-536359), which is incorporated herein by reference in its entirety.

Some embodiments may involve using a gene set that includes genes that are up-regulated in angioimmunoblastic T-cell lymphoma (AITL) compared to normal T lymphocytes, which may be referred to herein as “up-regulated in AITL genes”. For example, one or more genes in the gene set PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_UP, with the systematic name M12225 in the Gene Set Enrichment Analysis (GSEA) database, may be used in determining PTCL subtype according to the techniques described herein. In some embodiments, the gene set may include one or more genes selected from the group consisting of: A2M, ABCC3, ABI3BP, ACKR1, ACTA2, ACVRL1, ADAMDEC1, ADAMTS1, ADAMTS9, ADGRF5, ADGRL4 ADRA2A, ANK2, ANKRD29, ANTXR1, APOC1, APOE, ARHGAP29, ARHGAP42, ARHGEF10, ASPM, ATOX1, C1QA, C1QB, C1QC, C1R, C1S, C2, C3, C4A, C7, CALD1, CARMN, CAV2, CAVIN1, CCDC102B, CCDC80, CCL14, CCL19, CCL2, CCL21, CCN4, CD63, CD93, CDH11, CDH5, CETP, CFB, CFH, CHI3L1, CLMP, CLU, CMKLR1, COL12A1, COL15A1, COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL6A1, COL8A2, COX7A1, CP, CSRP2, CTHRC1, CTSC, CTSL, CTTNBP2NL, CXCL10, CXCL12, CXCL9, CYBRD1, CYFIP1, CYPLB1, CYP26B1, CYP27A1, DAB2, DCLK1, DDR2, DEPP1, DHRS7B, DOCK4, DPYSL3, EMCN, EMILIN1, ENG, ENPP2, EPHX1, FAM107A, FAM114A1, FAM20A, FBN1, FCHO2, FERMT2, FLRT2, FN1, FSTL1, FUCA1, GABBR1, GASK1B, GJA1, GJC1, GPNMB, GPRC5B, GUCY1B1, HNMT, HSPB8, HSPG2, IDH1, IFI27, IGFBP5, IGFBP7, IL18, IL33, IRAK3, ITGA9, ITPRIPL2, KCNJ10, KCNMA1, KCTD12, LAMA4, LAMB1, LAMC1, LIFR, LOXL1, LPAR1, LUM, MARCKS, MFAP4, MIR1245A, MIR34AHG, MMP9, MXRA5, MYL9, MYLK, NAGK, NEXN, NFIB, NNMT, NPL, NR1H3, NR2F2, OSMR, P2RYl3, PAPSS2, PARVA, PCOLCE, PDGFRA, PDLIM5, PDPN, PGF, PLA2G2D, PLA2G4C, PLD1, PLPP3, PMP22, PPIC, PRRX1, PTGDS, RAB13, RA114, RARRES2, RASSF4, RBP5, RBPMS, RGL1, RGS5, RHOBTB3, RND3, RPE, RRAS, RSPO3, S1PR3, SAMD9L, SEPTIN10, SERPING1, SERPINH1, SLAMF8, SLC1A3, SLC40A1, SLCO2B1, SMOC2, SPARC, SPARCL1, SPRED1, SULF1, TAGLN, TANC1, TCIM, TDO2, TEAD2, THY1, TJP1, TLR4, TMEM163, TMEM176A, TMEM176B, TNC, TNS1, TNS3, TPM1, TRIM47, VCAM1, VWF, WDFY3, WLS, WWTR1, YAP1, and ZNF226.

Some embodiments may involve using a gene set that includes genes that are down-regulated in angioimmunoblastic lymphoma (AITL) compared to normal T lymphocytes, which may be referred to herein as “down-regulated in AITL genes”. For example, one or more genes in the gene set PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN, with the systematic name M4781 in the Gene Set Enrichment Analysis (GSEA) database, may be used in determining PTCL subtype according to the techniques described herein. In some embodiments, the gene set may include one or more genes selected from the group consisting of: AMD1, AREG, ATP2B1-AS1, B3GNT2, BOLA2, BTG1, C16orf72, CBX4, CCDC59, CCNL1, CD6, CD69, CHD1, CLK1, CNOT6L, CNST, COG3, CREM, CSGALNACT2, CSRNP1, DDX3X, DNAJB6, DUSP10, DUSP2, DUSP4, EIFI, EIF4E, EIF4G3, EIF5, EPC1, ETNK1, FBXO33, FBXW7, FOSB, FOSL2, FOXPI, G3BP2, GABARAPL1, GADD45A, GADD45B, GATA3, H2AC18, H3-3B, HAUS3, HECA, HIPK1, ID2, IDS, IER5, IFRD1, IKZF5, ING3, IRF2BP2, IRS2, JMJD1C, JMY, JUN, JUND, KDM3A, KDM6B, KLF10, KLF4, KLF6, LINC-PINT, LINC01578, LY9, MAP3K8, MCL1, MEX3C, MGAT4A, MOAP1, MPZL3, MXD1, MYLIP, NAMPT, NDUFA10, NR4A2, NR4A3, PCIF1, PDE4D, PELI1, PER1, PHF1, PIGA, PMAIP1, PNPLA8, PPPIR15A, PPPIR15B, PRNP, PTGER4, PTP4A1, PTP4A2, RAPGEF6, REL, RGCC, RGS1, RGS2, RNF103, RNF11, RNF139, RSRC2, SARAF, SBDS, SETD2, SIK1, SIK3, SLC2A3, SLC30A1, SMURF2, SNORD22, SNORD3B-1, SON, SRSF5, STK17B, SUCO, THAP2, TIPARP, TMX4, TNFAIP3, TOB1, TP53INP2, TRA2B, TSC22D2, TSC22D3, TSPYL2, TTC7A, TUBB2A, WIPF1, YPEL5, ZBTB10, ZBTB24, ZFAND2A, ZFAND5, ZFC3H1, ZFP36, and ZNF331.

Some embodiments may involve using a gene set that includes genes associated with a molecular functional (MF) profile of a subject, which may be referred to herein as “MF profile genes”. In some embodiments, genes associated with a MF profile may include genes in one or more modules of the MF profile. Examples of genes associated with a MF profile and modules of a MF profile are described and listed in U.S. Pat. No. 10,311,967, titled “SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTION PROFILES,” issued on Jun. 4, 2019, which is incorporated herein by reference in its entirety. In some embodiments, one or more of the genes associated with a MF profile and one or more of the genes listed in Table 10 may be used in combination as a gene set for determining a PTCL subtype.

Some embodiments may involve determining a PTCL subtype for cells in a biological sample by using a statistical model that outputs multiple PTCL subtype predictions corresponding to different PTCL subtypes, which are used to determine a PTCL subtype for the biological sample. FIG. 26 is a diagram of an illustrative processing pipeline 2600 for determining a PTCL subtype of a biological sample, which may include ranking genes based on their gene expression levels and using the ranking and statistical model to determine the PTCL subtype, in accordance with some embodiments of the technology described herein. Processing pipeline 2600 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, processing pipeline 2600 may be performed by a desktop computer, a laptop computer, a mobile computing device. In some embodiments, processing pipeline 2600 may be performed within one or more computing devices that are part of a cloud computing environment.

In some embodiments, gene expression data 102 and ranking process 108 are used to rank genes based on their expression levels in gene expression data 102 to obtain gene ranking 110. Gene ranking 110 may be input to statistical model 112. Statistical model 112 may be trained using training data indicating rankings of expression levels for some or all genes in the gene set.

In some embodiments, statistical model 112 may output predictions of the biological sample having particular PTCL subtypes. In some instances, a prediction output by a statistical model may include a probability of the biological sample having the PTCL subtype. As shown in FIG. 26 , statistical model 112 outputs PTCL Subtype Prediction 1 216 a, PTCL Subtype Prediction 2 216 b, PTCL Subtype Prediction 1 216 c, and PTCL Subtype Prediction 1 216 d. The predictions output by statistical model 112 may be analyzed using prediction analysis process 118 to determine PTCL subtype 214 for the biological sample. Prediction analysis process 118 may involve selecting a particular PTCL subtype for the biological sample from among the different PTCL subtype predictions. In some embodiments, a PTCL subtype prediction may include a probability that the biological sample has the particular PTCL subtype. In such embodiments, prediction analysis process 118 may involve selecting a PTCL subtype based on the probabilities. In some embodiments, selecting the PTCL subtype may involve selecting the PTCL subtype having the highest probability as being PTCL subtype 214.

In some embodiments, statistical model 112 may provide outputs, each corresponding to a different PTCL subtype. For example, PTCL Subtype Prediction 1 216 a may correspond to anaplastic large cell lymphoma (ALCL), PTCL Subtype Prediction 2 216 b may correspond to angioimmunoblastic T-cell lymphoma (AITL), PTCL Subtype Prediction 3 216 c may correspond to natural killer/T-cell lymphoma (NKTCL), and PTCL Subtype Prediction 4 216 d may correspond to adult T-cell leukemia/lymphoma (ATLL). In some embodiments, statistical model 112 may include a multi-class classifier. In some embodiments, a class weight may be implemented for one or more of the classes in the multi-class classifier. Examples of classifiers that statistical model 112 may include are a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier.

Although four outputs from statistical model 112 are shown in FIG. 26 , it should be appreciated that a statistical model having any suitable number of outputs for PTCL subtype predictions may be implemented using the techniques described above in determining a PTCL subtype of a biological sample. In some embodiments, the outputs may be in the range of 3 to 5, 3 to 10, 3 to 15, or 3 to 20.

Some embodiments may involve determining a PTCL subtype for cells in a biological sample by using multiple statistical models that correspond to different PTCL subtypes and output predictions for the PTCL subtypes, which are used to determine a PTCL subtype for the biological sample. FIG. 27 is a diagram of an illustrative processing pipeline 2700 for determining a PTCL subtype of a biological sample, which may include ranking genes based on their gene expression levels and using the ranking and statistical models to determine the PTCL subtype, in accordance with some embodiments of the technology described herein. Processing pipeline 2700 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, processing pipeline 2700 may be performed by a desktop computer, a laptop computer, a mobile computing device. In some embodiments, processing pipeline 2700 may be performed within one or more computing devices that are part of a cloud computing environment.

In some embodiments, gene expression data 102 and ranking process 108 are used to rank genes based on their expression levels in gene expression data 102 to obtain gene ranking 110. Gene ranking 110 may be input to statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d. Each of statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may be trained using training data indicating rankings of expression levels for some or all genes in the gene set. Statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may each correspond to a different PTCL subtype and output a prediction of the biological sample having its particular PTCL subtype. In some instances, the prediction output by a statistical model may include a probability of the biological sample having the PTCL subtype.

As shown in FIG. 27 , statistical model 1 112 a outputs PTCL Subtype Prediction 1 316 a, statistical model 2 112 b outputs PTCL Subtype Prediction 2 316 b, statistical model 3 112 c outputs PTCL Subtype Prediction 3 316 c, and statistical model 4 112 d outputs PTCL Subtype Prediction 4 316 d. Each of statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may correspond to a different PTCL subtype. For example, statistical model 1 112 a and PTCL Subtype Prediction 1 316 a may correspond to anaplastic large cell lymphoma (ALCL) and statistical model 1 112 a may be trained using rankings of expression levels for one or more genes associated with ALCL, such as those listed in Table 11. As another example, statistical model 2 112 b and PTCL Subtype Prediction 2 316 b may correspond to angioimmunoblastic T-cell lymphoma (AITL) and statistical model 2 112 b may be trained using rankings of expression levels for one or more genes associated with AITL, such as those listed in Table 11. As yet another example, statistical model 3 112 c and PTCL Subtype Prediction 3 316 c may correspond to natural killer/T-cell lymphoma (NKTCL) and statistical model 3 112 c may be trained using rankings of expression levels for one or more genes associated with NKTCL, such as those listed in Table 11. As another example, statistical model 4 112 d and PTCL Subtype Prediction 4 316 d may correspond to adult T-cell leukemia/lymphoma (ATLL) and statistical model 4 112 d may be trained using rankings of expression levels for one or more genes associated with ATLL, such as those listed in Table 11.

The predictions output by statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may be analyzed using prediction analysis process 118 to determine PTCL subtype 214 for the biological sample. Prediction analysis process 118 may involve selecting a particular PTCL subtype for the biological sample from among the different PTCL subtype predictions. In some embodiments, a PTCL subtype prediction may include a probability that the biological sample has the particular PTCL subtype. In such embodiments, prediction analysis process 118 may involve selecting a PTCL subtype based on the probabilities. In some embodiments, selecting the PTCL subtype may involve selecting the PTCL subtype having the highest probability as being PTCL subtype 214.

In some embodiments, one or more of statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may include a binary classifier. In some embodiments, each of statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d includes a binary classifier. In such embodiments, if none of the binary classifiers used are not determinative as to which class the biological sample belongs, then the sample may be determined to be unclassified. In some embodiments, statistical model 1 112 a, statistical model 2 112 b, statistical model 3 112 c, and statistical model 4 112 d may have a hierarchical classifier configuration.

Some embodiments may involve a hierarchical configuration of four classifiers in the order of a first classifier for the NKTCL PTCL subtype, a second classifier for the ATLL PTCL subtype, a third classifier for the AITL PTCL subtype, a fourth classifier for the ALCL PTCL subtype. In some embodiments, each of the first, second, third, and fourth classifiers is a binary classifier.

Although four statistical models and corresponding outputs are shown in FIG. 27 , it should be appreciated that any number of statistical models may be implemented using the techniques described above in determining a PTCL subtype of a biological sample. In some embodiments, the number of statistical models may be in the range of 3 to 5, 3 to 10, 3 to 15, or 3 to 20.

Some embodiments may involve determining a PTCL subtype of a biological sample by using different gene sets and statistical models corresponding to the different gene sets to obtain PTCL subtype predictions, which are used to determine the PTCL subtype. FIG. 28 is a diagram of an illustrative processing pipeline 2800 for determining a PTCL subtype of a biological sample, which may include ranking genes based on their gene expression levels and using the rankings and statistical models to determine the PTCL subtype, in accordance with some embodiments of the technology described herein. Processing pipeline 2800 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, processing pipeline 2800 may be performed by a desktop computer, a laptop computer, a mobile computing device. In some embodiments, processing pipeline 2800 may be performed within one or more computing devices that are part of a cloud computing environment.

In some embodiments, gene expression data 102 is used to rank genes in different sets of genes based on their expression levels in gene expression data 102 to obtain multiple gene rankings. For example, a gene ranking may be obtained for each gene set and the gene ranking may be input to a statistical model trained using training data indicating rankings of expression levels for some or all genes in the gene set. As shown in FIG. 28 , ranking process 108 may involve using expression data 102 to rank genes in different gene sets, including Gene Set 1 106 a, Gene Set 2 106 b, Gene Set 3 106 c, and Gene Set 4 106 d, to obtain Gene Ranking 1 110 a, Gene Ranking 2 110 b, Gene Ranking 3 110 c, and Gene Ranking 4 110 d, respectively. Ranking process 108 may involve ranking genes in a set of genes based on numerical values of their expression levels. Different gene rankings may be obtained by ranking expression levels for different gene sets, and each gene ranking may be input to its respective statistical model to obtain a PTCL subtype prediction. As shown in FIG. 28 , Gene Ranking 1 10 a, Gene Ranking 2 110 b, Gene Ranking 3 110 c, and Gene Ranking 4 110 d is provided as input to Statistical Model 1 112 a, Statistical Model 2 112 b, Statistical Model 3 112 c, and Statistical Model 4 112 d, respectively.

In some embodiments, the different statistical models and their respective gene sets may correspond to a particular PTCL subtypes of the biological sample. In such embodiments, each of the statistical models may output a prediction of the biological sample having a particular PTCL subtype. In some instances, the prediction output by a statistical model may include a probability of the biological sample having the PTCL subtype.

As shown in FIG. 28 , Statistical Model 1 112 a outputs PTCL Subtype Prediction 1 416 a, Statistical Model 2 112 b outputs PTCL Subtype Prediction 2 416 b, Statistical Model 3 112 c outputs PTCL Subtype Prediction 3 416 c, and PTCL Subtype Prediction 4 116 d outputs PTCL Subtype Prediction 4 416 d. The predictions output by the different statistical models may be analyzed using prediction analysis process 118 to determine PTCL subtype 114 for the biological sample.

Although four gene sets and four statistical models are shown in FIG. 28 , it should be appreciated that any suitable number of gene sets and corresponding statistical models may be implemented using the techniques described above in determining PTCL subtype predictions to obtain a PTCL subtype of a biological sample. In some embodiments, the number of gene sets and corresponding statistical models may be in the range of 3 to 100, 3 to 70, 3 to 50, 3 to 40, 3 to 30, 5 to 50, 10 to 60, or 10 to 70.

In some embodiments, the number of gene sets and corresponding statistical models is equal to or less than the number of classes for the PTCL subtype. Such embodiments may involve a different gene set and corresponding statistical model for each PTCL subtype. For example, Gene Set 1 106 a and Statistical Model 1 112 a may be used for generating a prediction of the PTCL subtype being anaplastic large cell lymphoma (ALCL) (as PTCL Subtype Prediction 1 416 a), Gene Set 2 106 b and Statistical Model 2 112 b may be used for generating a prediction of the PTCL subtype being angioimmunoblastic T-cell lymphoma (AITL) (as PTCL Subtype Prediction 2 416 b), Gene Set 3 106 c and Statistical Model 3 112 c may be used for generating a prediction of the PTCL subtype being natural killer/T-cell lymphoma (NKTCL) (as PTCL Subtype Prediction 3 416 c), and Gene Set 4 106 d and Statistical Model 4 112 d may be used for generating a prediction of the PTCL subtype being adult T-cell leukemia/lymphoma (ATLL) (as PTCL Subtype Prediction 4 416 d). It should be appreciated that additional gene sets and their corresponding statistical models may be implemented for different tissue types.

FIG. 29 is a flow chart of an illustrative process 2900 for determining one or more characteristics of a biological sample using a gene ranking and a statistical model, in accordance with some embodiments of the technology described herein. Process 2900 may be performed on any suitable computing device(s) (e.g., a single computing device, multiple computing devices co-located in a single physical location or located in multiple physical locations remote from one another, one or more computing devices part of a cloud computing system, etc.), as aspects of the technology described herein are not limited in this respect. In some embodiments, ranking process 108 and statistical model 112 may perform some or all of process 2900 to determine PTCL subtype.

Process 2900 begins at act 2910, where expression data for a biological sample of a subject is obtained. In some embodiments, the expression data may be obtained using a gene expression microarray. In some embodiments, the expression data may be obtained by performing next generation sequencing. In some embodiments, the expression data may be obtained by using a hybridization-based expression assay. Some embodiments involve performing a sequencing process of the biological sample (e.g., a gene expression microarray, next generation sequencing) prior to obtaining expression data 102. In some embodiments, obtaining gene expression data 102 may involve obtaining gene expression data 102 in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way. In some embodiments, obtaining gene expression data 102 may involve analyzing a biological sample (in vitro) and accessing (e.g., by a computing device, processor) the expression data. Further aspects relating to obtaining expression data are provided in the section titled “Obtaining Expression Data”.

Next, process 2900 proceeds to act 2920, where genes in a set of genes are ranked based on their expression levels in the expression data to obtain a gene ranking, such as by using ranking process 108. The expression data may include values, each representing an expression level for a gene in the set of genes, and determining the gene ranking may involve determining a relative rank for each gene in the set of genes based on the values.

In some embodiments, the subject has, is suspected of having, or is at risk of having breast cancer. The set of genes may be selected from the group of genes listed in Table 10. The set of genes may include at least 3, 5, 10, or 20 genes selected from the group of genes listed in Table 10. In some embodiments, the set of genes may include all the genes listed in Table 10. In some embodiments, the set of genes may include 3-120 genes, 5-120 genes, 20-120 genes, 50-120 genes, 80-120 genes listed in Table 10. In some embodiments, the set of genes may include 120 or fewer genes, 100 or fewer genes, 80 or fewer genes, 50 or fewer genes, 20 or fewer genes listed in Table 10.

In some embodiments, the subject has, is suspected of having, or is at risk of having lymphoma. In some embodiments, the subject has, is suspected of having, or is at risk of having PTCL.

Next process 2900 proceeds to act 2920, where PTCL subtype of the biological sample is determined using the gene ranking and a statistical model, such as statistical model 112. The statistical model may be trained using rankings of expression levels for one or more genes in the set of genes. In some embodiments, the gene ranking may be used as an input to the statistical model to obtain an output indicating the PTCL subtype. In some embodiments, the statistical model comprises one or more classifiers selected from the group consisting of: a statistical model may include are a gradient boosted decision tree classifier, a decision tree classifier, a gradient boosted classifier, a random forest classifier, a clustering-based classifier, a Bayesian classifier, a Bayesian network classifier, a neural network classifier, a kernel-based classifier, and a support vector machine classifier. In some embodiments, a statistical model may involve using a machine learning algorithm that implements of a gradient boosting framework, such as a gradient boosting decision tree (GBDT) and a gradient boosted regression tree (GBRT). Examples of software packages that implement machine learning algorithms that may be used according to the techniques described herein include the LightGBM package, the XGBoost package, and the pGBRT package.

In some embodiments, the statistical model may include a multi-class classifier. The multi-class classifier may provide at least four outputs each corresponding to a different PTCL subtype. For example, a first output may correspond to anaplastic large cell lymphoma (ALCL), a second output may correspond to angioimmunoblastic T-cell lymphoma (AITL), a third output may correspond to natural killer/T-cell lymphoma (NKTCL), and a fourth output may correspond to adult T-cell leukemia/lymphoma (ATLL).

In some embodiments, the statistical model may include multiple classifiers corresponding to different PTCL subtypes. For example, a first classifier may correspond anaplastic large cell lymphoma (ALCL), a second classifier may correspond to angioimmunoblastic T-cell lymphoma (AITL), a third classifier may correspond to natural killer/T-cell lymphoma (NKTCL), and a fourth classifier may correspond to adult T-cell leukemia/lymphoma (ATLL). In some embodiments, the multiple classifiers may be binary classifiers. The binary classifiers may have a hierarchical classification. For example, a statistical mode may include four binary classifiers having a hierarchical configuration in the order of a first classifier for the NKTCL PTCL subtype, a second classifier for the ATLL PTCL subtype, a third classifier for the AITL PTCL subtype, a fourth classifier for the ALCL PTCL subtype.

In some embodiments, the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL). In some embodiments, the subtype of PTCL is selected from the group consisting of: Peripheral T-Cell Lymphoma, Not Otherwise Specified (PTCL-NOS), anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), Natural killer/T-cell lymphoma (NKTCL), Sezary syndrome, adult T-cell leukemia/lymphoma (ATLL), enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, hepatosplenic gamma-delta T-cell lymphoma, T-cell lymphomas of Follicular T-cell (TFH) origin, T-cell lymphomas of the gastrointestinal tract, and cutaneous T-cell lymphomas.

In some embodiments, process 2900 may include outputting the PTCL subtype to a user (e.g., physician), such as by displaying the PTCL subtype to the user on a graphical user interface (GUI), including the PTCL subtype in a report, sending an email to the user, and in any other suitable way.

In some embodiments, process 2900 may include administering a treatment to the subject based on the determined PTCL subtype of the biological sample. For example, a physician may administer a treatment for the subject associated with treating lymphomas of the determined PTCL subtype. Further examples where PTCL subtype of a biological sample determined using the techniques described herein are used for administering a treatment are provided in the section titled “Methods of Treatment”.

In some embodiments, process 2900 may include identifying a treatment for the subject based on the determined PTCL subtype. For example, the determined PTCL subtype may be used to identify a treatment for the subject associated with treating lymphomas of the determined PTCL subtype.

In some embodiments, process 2900 may include determining a prognosis for the subject based on the determined PTCL subtype. For example, the determined PTCL subtype may be used to determine a prognosis for the subject associated with treating lymphomas of the determined PTCL subtype.

Further aspects relating to other applications where PTCL subtype of a biological sample determined using the techniques described herein are provided in the section titled “Applications”.

In some embodiments, a trained statistical model used for determining PTCL subtype may be evaluated using existing clinical data to determine its performance in identifying PTCL subtype. As an example, a gene set having the genes listed in Table 10 was used for rank process 108 and a multi-class classifier was used for determining whether samples belong to AITL, ATLL, ALCL, NKTCL, or PTCL NOS subtypes. The clinical data listed in Table 9 was used for this evaluation process and Table 12, below, shows the PTCL subtypes identified using this process. The statistical model used achieved a 0.84 f1 score. FIG. 30 is a plot of survival rates for the different PTCL subtypes (ATLL, ALCL, NKTCL, and PTCL NOS).

TABLE 12 PTCL Subtype Classification Performance. Cohort AITL ATLL ALCL NKTCL PTCL NOS GSE58445 15 0 9 7 160 n = 191 GSE45712 10 0 12 0 80 n = 101 GSE19069 29 11 24 0 36 n = 100 GSE90597 0 0 0 66 0 n = 66 GSE6338 6 0 6 0 28 n = 40 GSE36172 0 0 0 0 38 n = 38 E-TABM-783 17 0 0 0 16 n = 33 GSE65823 0 0 31 0 0 n = 31 GSE118238 0 0 29 0 0 n = 29 E-TABM-702 0 0 0 7 16 n = 23 GSE78513 0 0 23 0 0 n = 23 GSE51521 20 0 0 0 0 n = 20 GSE14317 0 19 0 0 0 n = 19 GSE80631 0 0 0 19 0 n = 19 GSE19067 0 0 0 18 0 n = 18 GSE20874 8 0 0 4 6 n = 18 SRP049695 0 0 0 17 0 n = 17 SRP029591 10 0 0 0 0 n = 10

In some aspects, methods for characterization of cancers as described herein may be applied to any lymphoma. “Lymphoma” generally refers to a cancer (e.g., neoplasm) that originates from lymph node and lymphoid cells. Lymphomas are typically classified according to the normal cell type from which the tumor cells originate, for example T-cell lymphomas, B-cell lymphomas, Hodgkin (lymphocyte) lymphomas, and histiocytic and dendritic cell neoplasms. Classification of lymphomas is described, for example, by Jiang et al. Expert Rev. Hematol. 2017 March; 10(3):239-249. Classification of PTCL lymphomas is described, for example, by Iqbal J, Wright G, Wang C, et al., Gene expression signatures delineate biological and prognostic subgroups in peripheral T-cell lymphoma, Blood, 2014; 123(19):2915-2923 (doi:10.1 182/blood-2013-11-536359), which is incorporated herein by reference in its entirety.

In some embodiments, a lymphoma is a B-cell lymphoma. In some embodiments, a B-cell lymphoma is a diffuse large B-cell lymphoma (DLBCL). Classification of DLBCL is described, for example, Alizadeh et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403, 503-511 (2000) (doi: 10.1038/35000501). Examples of DLBCLs include but are not limited to germinal center B-cell (GCB) subtype and activated B-cell (ABC) subtype.

In some embodiments, a lymphoma is a T-cell lymphoma. In some embodiments, a T-cell lymphoma is a mature T-cell lymphoma, such as a peripheral T-cell lymphoma (PTCL). Over 25 mature T-cell lymphomas have been identified. Examples of PTCLs include but are not limited to Peripheral T-Cell Lymphoma, Not Otherwise Specified (PTCL-NOS), anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), Natural killer/T-cell lymphoma (NKTCL), Sezary syndrome, adult T-cell leukemia/lymphoma (ATLL), enteropathy-type T-cell lymphoma, nasal NK/T-cell lymphoma, hepatosplenic gamma-delta T-cell lymphoma, T-cell lymphomas of Follicular T-cell (TFH) origin, T-cell lymphomas of the gastrointestinal tract (e.g., EATL, MEITL), cutaneous T-cell lymphomas, etc.

In some embodiments, the lymphoma is an anaplastic large cell lymphoma (ALCL). In some embodiments, the ALCL is systemic ALCL. In some embodiments, the ALCL is cutaneous ALCL (e.g., ALCL affecting the skin). In some embodiments, the ALCL is ALK-positive ALCL. In some embodiments, the ALCL is ALK-negative ALCL.

In some embodiments, the lymphoma is an angioimmunoblastic T-cell lymphoma (AITL). In some embodiments, AITL tumor cells express one or more follicular T cell markers, for example CD10 and CD279 (PD-1, PDCD1), CXCL13, BCL6, CD40L, or NFATC1.

In some embodiments, the lymphoma is an adult T-cell leukemia/lymphoma (ATLL). In some embodiments, ATLL results from infection with HTLV-1 virus.

In some embodiments, the lymphoma is a Natural killer/T-cell lymphoma (NKTCL). In some embodiments, NKTCL tumors are located in the palate and/or sinuses of a subject. In some embodiments, NKTCL tumors are located in the nasal cavity of a subject.

Obtaining Expression Data

Expression data (e.g., microarray data, next-generation sequencing (NGS) data) as described herein may be obtained from a variety of sources. In some embodiments, expression data may be obtained by analyzing a biological sample of a subject. The biological sample may be analyzed prior to performance of the techniques described herein, including the techniques for ranking genes based on their expression levels and using the ranking(s) to determine one or more characteristics of the biological sample. In some such embodiments, data obtained from the biological sample may be stored (e.g., in a database) and accessed during performance of the techniques described herein. Thus, “obtaining expression data” as described herein may involve obtaining gene expression data in silico, such as by accessing, using a computing device, expression data (e.g., expression data that has been previously obtained from a biological sample) in one or more data stores, receiving the expression data from one or more other device, or any other way, analyzing a biological sample (in vitro), or a combination thereof. Examples of additional techniques relating to how expression data is obtained are described in U.S. Pat. No. 10,311,967, titled “SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTION PROFILES,” issued on Jun. 4, 2019, which is incorporated herein by reference in its entirety.

In some embodiments, expression data may include expression levels for the entire cellular RNA, for all mRNAs in a cell, or a subset of RNAs in a cell (e.g., for a subset of RNAs expressed from a group of genes comprising or consisting of one or more gene sets described in this application, or at least some of the genes in those gene sets). RNA levels can be obtained using any appropriate technique including sequencing and/or hybridization based techniques (e.g., whole exome sequencing data, target specific sequencing data for a subset of RNAs, microarray data, etc.).

Biological Samples

Any of the methods, systems, assays, or other suitable techniques may be used to analyze any biological sample from a subject (e.g., a patient). In some embodiments, the biological sample may be any sample from a subject known or suspected of having cancer, including cancerous cells or pre-cancerous cells.

The biological sample may be any type of sample including, for example, a sample of a bodily fluid, one or more cells, a piece of tissue, or some or all of an organ. In some embodiments, the sample may be from a cancerous tissue or organ or a tissue or organ suspected of having one or more cancerous cells. In some embodiments, the sample may be from a healthy (e.g., non-cancerous) tissue or organ. In some embodiments, a sample from a subject (e.g., a biopsy from a subject) may include both healthy and cancerous cells and/or tissue. In certain embodiments, one sample will be taken from a subject for analysis.

Any of the biological samples described herein may be obtained from the subject using any known technique. In some embodiments, the biological sample may be obtained from a surgical procedure (e.g., laparoscopic surgery, microscopically controlled surgery, or endoscopy), bone marrow biopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g., a fine-needle aspiration, core needle biopsy, vacuum-assisted biopsy, or image-guided biopsy). In some embodiments, each of the biological samples is a bodily fluid sample, a cell sample, or a tissue biopsy. In some embodiments, one or more than one cell (a cell sample) is obtained from a subject using a scrape or brush method. The cell sample may be obtained from any area in or from the body of a subject including, for example, from one or more of the following areas: the cervix, esophagus, stomach, bronchus, or oral cavity. In some embodiments, one or more than one piece of tissue (e.g., a tissue biopsy) from a subject may be used. In certain embodiments, the tissue biopsy may comprise one or more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) samples from one or more tumors or tissues known or suspected of having cancerous cells.

Sample Analysis

Methods described herein are based, at least in part, on the identification and characterization of certain biological processes and/or molecular and cellular compositions that are present within and/or surrounding the cancer (e.g., the tumor).

Biological processes within and/or surrounding cancer (e.g., a tumor) include, but are not limited to, angiogenesis, metastasis, proliferation, cell activation (e.g., T cell activation), tumor invasion, immune response, cell signaling (e.g., HER2 signaling), and apoptosis.

Molecular and cellular compositions within and/or surrounding cancer (e.g., a tumor) include, but are not limited to, nucleic acids (e.g., DNA and/or RNA), molecules (e.g., hormones), proteins (e.g., wild-type and/or mutant proteins), and cells (e.g., malignant and/or non-malignant cells). The cancer microenvironment, as used herein, refers to the molecular and cellular environment in which the cancer (e.g., a tumor) exists including, but not limited to, blood vessels that surround and/or are internal to a tumor, immune cells, fibroblasts, bone marrow-derived inflammatory cells, lymphocytes, signaling molecules, and the extracellular matrix (ECM).

The molecular and cellular composition and biological processes present within and/or surrounding the tumor may be directed toward promoting cancer (e.g., tumor) growth and survival (e.g., pro-tumor) and/or inhibiting cancer (e.g., tumor) growth and survival (e.g., anti-tumor).

The cancer (e.g., tumor) microenvironment may comprise cellular compositions and biological processes directed toward promoting cancer (e.g., tumor) growth and survival (e.g., pro-tumor microenvironment) and/or inhibiting cancer (e.g., tumor) growth and survival (e.g., anti-tumor microenvironment). In some embodiments, the cancer (e.g., tumor) microenvironment comprises a pro-cancer (e.g., tumor) microenvironment. In some embodiments, the cancer (e.g., tumor) microenvironment comprises an anti-cancer (e.g., tumor) microenvironment. In some embodiments, the cancer (e.g., tumor) microenvironment comprises a pro-cancer (e.g., tumor) microenvironment and an anti-cancer (e.g., tumor) microenvironment.

Any information relating to molecular and cellular compositions, and biological processes that are present within and/or surrounding cancer (e.g., a tumor) may be used in methods for characterization of cancers (e.g., tumors) as described herein. In some embodiments, cancer (e.g., a tumor) may be characterized based on gene group expression level (e.g., on gene group RNA expression level). In some embodiments, cancer (e.g., a tumor) is characterized based on protein expression.

Methods for characterization of cancers as described herein may be applied to any cancer (e.g., any tumor). Exemplary cancers include, but are not limited to, adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, colon adenocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, prostate adenocarcinoma, rectal adenocarcinoma, skin cutaneous melanoma, stomach adenocarcinoma, thyroid carcinoma, uterine corpus endometrial carcinoma, and cholangiocarcinoma.

Expression Data

Expression data (e.g., indicating expression levels) for a plurality of genes may be used for any of the methods described herein. The number of genes which may be examined may be up to and inclusive of all the genes of the subject.

Any method may be used on a sample from a subject in order to acquire expression data (e.g., indicating expression levels) for the plurality of genes. As a set of non-limiting examples, the expression data may be RNA expression data, DNA expression data, or protein expression data.

DNA expression data, in some embodiments, refers to a level of DNA in a sample from a subject. The level of DNA in a sample from a subject having cancer may be elevated compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene duplication in a cancer patient's sample. The level of DNA in a sample from a subject having cancer may be reduced compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene deletion in a cancer patient's sample.

DNA expression data, in some embodiments, refers to data for DNA (or gene) expressed in a sample, for example, sequencing data for a gene that is expressed in a patient's sample. Such data may be useful, in some embodiments, to determine whether the patient has one or more mutations associated with a particular cancer.

RNA expression data may be acquired using any method known in the art including, but not limited to: whole transcriptome sequencing, total RNA sequencing, mRNA sequencing, targeted RNA sequencing, small RNA sequencing, ribosome profiling, RNA exome capture sequencing, and/or deep RNA sequencing. DNA expression data may be acquired using any method known in the art including any known method of DNA sequencing. For example, DNA sequencing may be used to identify one or more mutations in the DNA of a subject. Any technique used in the art to sequence DNA may be used with the methods described herein. As a set of non-limiting examples, the DNA may be sequenced through single-molecule real-time sequencing, ion torrent sequencing, pyrosequencing, sequencing by synthesis, sequencing by ligation (SOLiD sequencing), nanopore sequencing, or Sanger sequencing (chain termination sequencing). Protein expression data may be acquired using any method known in the art including, but not limited to: N-terminal amino acid analysis, C-terminal amino acid analysis, Edman degradation (including though use of a machine such as a protein sequenator), or mass spectrometry.

In some embodiments, the expression data comprises next-generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data. In some embodiments, the expression data comprises whole exome sequencing (WES) data. In some embodiments, the expression data comprises whole genome sequencing (WGS) data. In some embodiments, expression data comprises RNA Seq data (e.g., by performing RNA sequencing). In some embodiments, expression data comprises a combination of RNA Seq data and WGS data. In some embodiments, expression data comprises a combination of RNA Seq data and WES data.

Assays

Any of the biological samples described herein can be used for obtaining expression data using conventional assays or those described herein. Expression data, in some embodiments, includes gene expression levels. Gene expression levels may be detected by detecting a product of gene expression such as mRNA and/or protein.

In some embodiments, gene expression levels are determined by detecting a level of a protein in a sample and/or by detecting a level of activity of a protein in a sample. As used herein, the terms “determining” or “detecting” may include assessing the presence, absence, quantity and/or amount (which can be an effective amount) of a substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values and/or categorization of such substances in a sample from a subject.

The level of a protein may be measured using an immunoassay. Examples of immunoassays include any known assay (without limitation), and may include any of the following: immunoblotting assay (e.g., Western blot), immunohistochemical analysis, flow cytometry assay, immunofluorescence assay (IF), enzyme linked immunosorbent assays (ELISAs) (e.g., sandwich ELISAs), radioimmunoassays, electrochemiluminescence-based detection assays, magnetic immunoassays, lateral flow assays, and related techniques. Additional suitable immunoassays for detecting a level of a protein provided herein will be apparent to those of skill in the art.

Such immunoassays may involve the use of an agent (e.g., an antibody) specific to the target protein. An agent such as an antibody that “specifically binds” to a target protein is a term well understood in the art, and methods to determine such specific binding are also well known in the art. An antibody is said to exhibit “specific binding” if it reacts or associates more frequently, more rapidly, with greater duration and/or with greater affinity with a particular target protein than it does with alternative proteins. It is also understood by reading this definition that, for example, an antibody that specifically binds to a first target peptide may or may not specifically or preferentially bind to a second target peptide. As such, “specific binding” or “preferential binding” does not necessarily require (although it can include) exclusive binding. Generally, but not necessarily, reference to binding means preferential binding. In some examples, an antibody that “specifically binds” to a target peptide or an epitope thereof may not bind to other peptides or other epitopes in the same antigen. In some embodiments, a sample may be contacted, simultaneously or sequentially, with more than one binding agent that binds different proteins (e.g., multiplexed analysis).

It will be apparent to those of skill in the art that this disclosure is not limited to immunoassays. Detection assays that are not based on an antibody, such as mass spectrometry, are also useful for the detection and/or quantification of a protein and/or a level of protein as provided herein. Assays that rely on a chromogenic substrate can also be useful for the detection and/or quantification of a protein and/or a level of protein as provided herein.

Alternatively, the level of nucleic acids encoding a gene in a sample can be measured via a conventional method. In some embodiments, measuring the expression level of nucleic acid encoding the gene comprises measuring mRNA. In some embodiments, the expression level of mRNA encoding a gene can be measured using real-time reverse transcriptase (RT) Q-PCR or a nucleic acid microarray. Methods to detect nucleic acid sequences include, but are not limited to, polymerase chain reaction (PCR), reverse transcriptase-PCR (RT-PCR), in situ PCR, quantitative PCR (Q-PCR), real-time quantitative PCR (RT Q-PCR), in situ hybridization, Southern blot, Northern blot, sequence analysis, microarray analysis, detection of a reporter gene, or other DNA/RNA hybridization platforms.

In some embodiments, the level of nucleic acids encoding a gene in a sample can be measured via a hybridization assay. In some embodiments, the hybridization assay comprises at least one binding partner. In some embodiments, the hybridization assay comprises at least one oligonucleotide binding partner. In some embodiments, the hybridization assay comprises at least one labeled oligonucleotide binding partner. In some embodiments, the hybridization assay comprises at least one pair of oligonucleotide binding partners. In some embodiments, the hybridization assay comprises at least one pair of labeled oligonucleotide binding partners.

Any binding agent that specifically binds to a desired nucleic acid or protein may be used in the methods and kits described herein to measure an expression level in a sample. In some embodiments, the binding agent is an antibody or an aptamer that specifically binds to a desired protein. In other embodiments, the binding agent may be one or more oligonucleotides complementary to a nucleic acid or a portion thereof. In some embodiments, a sample may be contacted, simultaneously or sequentially, with more than one binding agent that binds different proteins or different nucleic acids (e.g., multiplexed analysis).

To measure an expression level of a protein or nucleic acid, a sample can be in contact with a binding agent under suitable conditions. In general, the term “contact” refers to an exposure of the binding agent with the sample or cells collected therefrom for suitable period sufficient for the formation of complexes between the binding agent and the target protein or target nucleic acid in the sample, if any. In some embodiments, the contacting is performed by capillary action in which a sample is moved across a surface of the support membrane.

In some embodiments, an assay may be performed in a low-throughput platform, including single assay format. In some embodiments, an assay may be performed in a high-throughput platform. Such high-throughput assays may comprise using a binding agent immobilized to a solid support (e.g., one or more chips). Methods for immobilizing a binding agent will depend on factors such as the nature of the binding agent and the material of the solid support and may require particular buffers. Such methods will be evident to one of ordinary skill in the art.

Genes

The various genes recited herein are, in general, named using human gene naming conventions. The various genes, in some embodiments, are described in publicly available resources such as published journal articles. The gene names may be correlated with additional information (including sequence information) through use of, for example, the NCBI GenBank® databases; the HUGO (Human Genome Organization) Gene Nomination Committee (HGNC) databases; the DAVID Bioinformatics Resource. The gene names may also be correlated with additional information through printed publications from the foregoing organizations, which are incorporated by reference herein for this purpose. It should be appreciated that a gene may encompass all variants of that gene. For organisms or subjects other than human subjects, corresponding specific-specific genes may be used. Synonyms, equivalents, and closely related genes (including genes from other organisms) may be identified using similar databases including the NCBI GenBank® databases described above.

Some embodiments involve using a gene set for predicting breast cancer grade, including the genes listed in Table 1. Some embodiments involve using a gene set for predicting kidney clear cell cancer grade, including the genes listed in Table 2. Some embodiments involve using a gene set for predicting tissue of origin for Diffuse Large B-Cell Lymphoma (DLBCL), such as germinal center B-cell (GCB) and activated B-cell (ABC), including the genes listed in Table 3. Some embodiments involve using a gene set for predicting PTCL subtype, including the genes listed in Table 10.

Applications

Methods for biological sample characterization, which may include tumor type characterization, as described herein may be used for various clinical purposes including, but not limited to, monitoring the progress of cancer in a subject, assessing the efficacy of a treatment for cancer, identifying patients suitable for a particular treatment, evaluating suitability of a patient for participating in a clinical trial and/or predicting relapse in a subject. Accordingly, described herein are diagnostic and prognostic methods for cancer treatment based on tumor type described herein.

Methods described herein can be used to evaluate the efficacy of a cancer treatment, such as those described herein, given the correlation between cancer type (e.g., tumor types) and cancer prognosis. For example, multiple biological samples, such as those described herein, can be collected from a subject to whom a treatment is performed either before and after the treatment or during the course of the treatment. The cancer type (e.g., the tumor type) in the biological sample from the subject can be determined using any of the methods described herein. For example, if the cancer type indicates that the subject has a poor prognosis and the cancer type changes to a cancer type indicative of a favorable prognosis after the treatment or over the course of treatment, it indicates that the treatment is effective.

In some embodiments, cancer types can also be used to identify a cancer that may be treatable using a specific anti-cancer therapeutic agent (e.g., a chemotherapy). To practice this method, the cancer type in a sample (e.g., a tumor biopsy) collected from a subject having cancer can be determined using methods described herein. If the cancer type is identified as being susceptible to treatment with an anti-cancer therapeutic agent, the method may further comprise administering to the subject having the cancer an effective amount of the anti-cancer therapeutic agent.

In some embodiments, the methods for cancer type characterization as described herein may be relied on in the development of new therapeutics for cancer. In some embodiments, the cancer type may indicate or predict the efficacy of a new therapeutic or the progression of cancer in a subject prior to, during, or after the administration of the new therapy.

In some embodiments, methods for cancer type characterization as described herein may be used to evaluate suitability of a patient for participating in a clinical trial. In some embodiments, the cancer type may be used to include patients in a clinical trial. In some embodiments, patients having a specified cancer grade (e.g., Grade 1) are included in a clinical trial. In some embodiments, patients having a specified tissue of origin for the cancer are included in a clinical trial. In some embodiments, the cancer type may be used to exclude patients in a clinical trial. In some embodiments, patients having a specified cancer grade (e.g., Grade 3) are excluded from a clinical trial. In some embodiments, patients having a specified tissue of origin are excluded from a clinical trial. In some embodiments, patients having a specified PTCL subtype are excluded from a clinical trial.

In some embodiments, the methods described herein may be used in monitoring progression of a patient's disease and identifying one or more treatments based on a stage of disease determined using the techniques described herein. In some embodiments, the monitoring occurs over a period of time where a first disease stage is identified for the patient at a first time and a second disease stage is identified for the patient at a second time. The second disease stage may be used to identify a different type of treatment. For example, in the context of using the techniques described herein for predicting cancer grade, monitoring a patient's disease and identifying different treatments based on stage of disease may involve obtaining first expression data obtained by sequencing a first biological sample of a subject (e.g., a subject having kidney cancer), determining a first cancer grade using the first expression data and a statistical model described herein, identifying or recommending a first treatment for the subject based on the first cancer grade, and optionally, administering the first treatment. Monitoring the patient's disease may further involve obtaining second expression data obtained by sequencing a second biological sample of the subject (e.g., a biological sample obtained from the subject at a different time than the first biological sample), determining a second cancer grade using the second expression data, identifying or recommending a second treatment for the subject based on the second cancer grade, and optionally, administering the second treatment. In some embodiments, the first cancer grade is different from the first cancer grade and the first treatment is different from the second treatment. In some embodiments, monitoring may be performed multiple times (e.g., along with multiple medical visits) to evaluate progress of a treatment, determine how a patient is responding to a particular treatment, or a combination thereof.

In some embodiments, the methods described herein may be used in assessing how a subject has responded to a treatment. For example, these techniques described herein may be used in determining whether a subject is responding to a line of treatment or not, whether a subject is in remission, and whether there is a recurrence of a disease.

In some embodiments, characteristic(s) for cells of a biological sample of a subject determined using the techniques described herein may be used in identifying a diagnosis for the subject. In some embodiments, the characteristic(s) may provide information for a physician or other user to determine a diagnosis for the subject. For example, the characteristic(s) alone may be sufficient to allow a physician to determine the diagnosis. In some embodiments, a combination of the characteristic(s) and other patient medical data may be used by a physician or other user in determining a diagnosis for the subject.

In some embodiments, characteristic(s) for cells of a biological sample of a subject determined using the techniques described herein may be used in identifying a prognosis for the subject. In some embodiments, the characteristic(s) may provide information for a physician or other user to determine a prognosis for the subject. For example, the characteristic(s) alone may be sufficient to allow a physician to determine the prognosis. In some embodiments, a combination of the characteristic(s) and other patient medical data may be used by a physician or other user in determining a prognosis for the subject.

In some embodiments, a diagnosis or prognosis determined using the techniques described herein may be used in recommending a treatment or therapy for the subject. The therapy may be a drug treatment, radiation, surgery, diet or lifestyle change, or other therapy. A treatment may be chemotherapy, immunotherapy, hormone therapy, or other treatment. In some embodiments, recommending a treatment or therapy may include a change in treatment (e.g., a different treatment, an additional treatment, or a different frequency or dose).

In some embodiments, a diagnosis or prognosis determined using the techniques described herein may be used in generating a recommendation for further analysis of the patient. For example, a recommendation for further diagnostic intervention (e.g., more extensive CAT scan, MRI, more extensive or invasive biopsies, more detailed genetic, proteomic, or histological analysis of one or more tissue samples, etc.).

In some embodiments, a diagnosis or prognosis determined using the techniques described herein may be used in generating a recommendation to change the frequency of follow up medical checks. For example, a recommendation to have more frequent medical checks if the analysis suggests a higher risk, or less frequent medical checks if the analysis suggests a lower risk or that the subject is in remission.

In some embodiments, characteristic(s) for cells of a biological sample of a subject determined using the techniques described herein may be using in generating a report specific to the subject. For example, the report may be a patient-specific cancer characteristics report. Generating the report may involve generating a file comprising information indicative of disease characteristics determined using the techniques described herein (e.g., cancer grade, tissue of origin, tissue subtype).

In the context of providing a recommendation or other information to a physician or other user, providing such information may involve transmitting electronic information to the physician or other user. In some embodiments, the electronic information may be transmitted to a medical center or to a computer system that hosts the patient medical information, and the physician or other user may access the information using a computing device.

Examples of additional applications for how characteristics of a biological sample, as determined using the techniques described herein, may be used are described in in U.S. Pat. No. 10,311,967, titled “SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTION PROFILES,” issued on Jun. 4, 2019, which is incorporated herein by reference in its entirety.

Methods of Treatment

In certain methods described herein, an effective amount of anti-cancer therapy described herein may be administered or recommended for administration to a subject (e.g., a human) in need of the treatment via a suitable route (e.g., intravenous administration).

The subject to be treated by the methods described herein may be a human patient having, suspected of having, or at risk for a cancer. A subject having, suspected of having, or at risk of having cancer may be a subject exhibiting one or more signs or symptoms of cancer, subject that is diagnosed as having cancer, a subject that has a family history and/or a genetic predisposition to having cancer, and/or a subject that has one or more other risk factors for cancer (e.g., age, exposure to carcinogens, environmental exposure, exposure to a virus associated with a higher likelihood of developing cancer, etc.). Examples of a cancer include, but are not limited to, melanoma, lung cancer, brain cancer, breast cancer, colorectal cancer, pancreatic cancer, liver cancer, prostate cancer, skin cancer, kidney cancer, bladder cancer, or prostate cancer. The subject to be treated by the methods described herein may be a mammal (e.g., may be a human). Mammals include, but are not limited to: farm animals (e.g., livestock), sport animals, laboratory animals, pets, primates, horses, dogs, cats, mice, and rats.

“An effective amount” as used herein refers to the amount of each active agent required to confer therapeutic effect on the subject, either alone or in combination with one or more other active agents. Effective amounts vary, as recognized by those skilled in the art, depending on the particular condition being treated, the severity of the condition, the individual patient parameters including age, physical condition, size, gender and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment. It will be understood by those of ordinary skill in the art, however, that a patient may insist upon a lower dose or tolerable dose for medical reasons, psychological reasons, or for virtually any other reasons.

Examples of additional methods of treatment are described in U.S. Pat. No. 10,311,967, titled “SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTION PROFILES,” issued on Jun. 4, 2019, which is incorporated herein by reference in its entirety.

Quality Control Analysis

In some embodiments, the techniques described herein may be used in performing quality control. One application is quality control analysis in a laboratory setting. For example, a sequencing laboratory may receive a biological sample together with information about the biological sample. Aside from an identifier and/or tracking number, such information may include information about the characteristics of the biological sample (e.g., the tissue source, cancer type, cancer grade, etc.). However, due to laboratory errors, it is possible that the biological sample provided does not actually have these characteristics (e.g., due to an error where patient samples are switched, mislabeled, wrong information is provided, etc.).

Another application is to quality control analysis in data analysis setting. For example, a patient's sequencing data (e.g., reads, aligned reads, expression levels, etc.) may be provided as input to a data processing pipeline. However, if that sequencing data does not correspond to the alleged source (e.g., it comes from a different patient due to an error), the results of the analysis are likely meaningless.

In some embodiments, quality control may be performed by comparing an asserted characteristic of a biological sample to a predicted characteristic determined using the techniques described herein. When the asserted characteristic and the predicted characteristic match (e.g., are the same or are within a tolerated difference), then it may be determined that a quality control check has been satisfied. On the other hand, if the predicted and asserted characteristics do not match, then further action may need to be taken. For example, further analysis of the biological sample may be performed, the biological sample may be rejected, a data processing pipeline may be stopped or not executed (thereby saving valuable and costly computational resources), a laboratory operator and/or other party (e.g., clinician, staff, etc.) may be notified of a potential discrepancy (e.g., by an e-mail alert, a message, a report, an entry in a log-file, etc.).

For example, a classifier for determining cancer grade may be used to predict cancer grade from gene expression data of a sample, and the predicted cancer grade may be compared to an asserted cancer grade for the sample. If the predicted and asserted cancer grades match, then it may be determined that the sample analysis has met quality control standards. However, if the predicted and asserted cancer grades do not match, then further analysis may be performed. As another example, a classifier for determining tissue of origin may be used to predict a type of tissue for a sample and the predicted tissue type may be compared to an asserted tissue type for the sample. If the predicted and asserted tissue types do not match, then further analysis of the biological sample may be performed to identify the tissue type for the sample. Any of the classification techniques described herein may be used in this manner, either alone or in combination with one another to provide multiple quality control checkpoints.

Examples of additional quality control analysis are described in U.S. patent application Ser. No. 16/920,636, titled “TECHNIQUES FOR BIAS CORRECTION IN SEQUENCE DATA,” filed Jul. 3, 2020, which is incorporated herein by reference in its entirety.

Computational System

An illustrative implementation of a computer system 1000 that may be used in connection with any of the embodiments of the technology described herein is shown in FIG. 10 . The computer system 1000 includes one or more processors 1010 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 1020 and one or more non-volatile storage media 1030). The processor 1010 may control writing data to and reading data from the memory 1020 and the non-volatile storage device 1030 in any suitable manner, as the aspects of the technology described herein are not limited in this respect. To perform any of the functionality described herein, the processor 1010 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 1020), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 1010.

Computing device 1000 may also include a network input/output (I/O) interface 1040 via which the computing device may communicate with other computing devices (e.g., over a network), and may also include one or more user I/O interfaces 1050, via which the computing device may provide output to and receive input from a user. The user I/O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and/or various other types of I/O devices.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed among multiple computing devices. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above.

In this respect, it should be appreciated that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible, non-transitory computer-readable storage medium) encoded with a computer program (e.g., a plurality of executable instructions) that, when executed on one or more processors, performs the above-discussed functions of one or more embodiments. The computer-readable medium may be transportable such that the program stored thereon can be loaded onto any computing device to implement aspects of the techniques discussed herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs any of the above-discussed functions, is not limited to an application program running on a host computer. Rather, the terms computer program and software are used herein in a generic sense to reference any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be employed to program one or more processors to implement aspects of the techniques discussed herein.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of processor-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the disclosure provided herein need not reside on a single computer or processor, but may be distributed in a modular fashion among different computers or processors to implement various aspects of the disclosure provided herein.

Processor-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in one or more non-transitory computer-readable storage media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a non-transitory computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements.

Also, various inventive concepts may be embodied as one or more processes, of which examples have been provided. The acts performed as part of each process may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

Aspects of the technology described herein provide computer implemented methods for generating, visualizing and classifying biological characteristic(s) (e.g., cancer grade, tissue of origin) of cancer patients.

In some embodiments, a software program may provide a user with a visual representation of a patient's characteristic(s) and/or other information related to a patient's cancer using an interactive graphical user interface (GUI). Such a software program may execute in any suitable computing environment including, but not limited to, a cloud-computing environment, a device co-located with a user (e.g., the user's laptop, desktop, smartphone, etc.), one or more devices remote from the user (e.g., one or more servers), etc.

For example, in some embodiments, the techniques described herein may be implemented in the illustrative environment 1100 shown in FIG. 11 . As shown in FIG. 11 , within illustrative environment 1100, one or more biological samples of a patient 1102 may be provided to a laboratory 1104. Laboratory 1104 may process the biological sample(s) to obtain expression data (e.g., DNA, RNA, and/or protein expression data) and provide it, via network 1108, to at least one database 1106 that stores information about patient 1102.

Network 1108 may be a wide area network (e.g., the Internet), a local area network (e.g., a corporate Intranet), and/or any other suitable type of network. Any of the devices shown in FIG. 11 may connect to the network 1108 using one or more wired links, one or more wireless links, and/or any suitable combination thereof.

In the illustrated embodiment of FIG. 11 , the at least one database 1106 may store expression data for the patient, medical history data for the patient, test result data for the patient, and/or any other suitable information about the patient 1102. Examples of stored test result data for the patient include biopsy test results, imaging test results (e.g., MRI results), and blood test results. The information stored in at least one database 1106 may be stored in any suitable format and/or using any suitable data structure(s), as aspects of the technology described herein are not limited in this respect. The at least one database 1106 may store data in any suitable way (e.g., one or more databases, one or more files). The at least one database 1106 may be a single database or multiple databases.

As shown in FIG. 11 , illustrative environment 1100 includes one or more external databases 1116, which may store information for patients other than patient 1102. For example, external databases 1116 may store expression data (of any suitable type) for one or more patients, medical history data for one or more patients, test result data (e.g., imaging results, biopsy results, blood test results) for one or more patients, demographic and/or biographic information for one or more patients, and/or any other suitable type of information. In some embodiments, external database(s) 1116 may store information available in one or more publicly accessible databases such as TCGA (The Cancer Genome Atlas), one or more databases of clinical trial information, and/or one or more databases maintained by commercial sequencing suppliers. The external database(s) 1116 may store such information in any suitable way using any suitable hardware, as aspects of the technology described herein are not limited in this respect.

In some embodiments, the at least one database 1106 and the external database(s) 1116 may be the same database, may be part of the same database system, or may be physically co-located, as aspects of the technology described herein are not limited in this respect.

For example, in some embodiments, server(s) 1110 may access information stored in database(s) 1106 and/or 1116 and use this information to perform process 300, described with reference to FIG. 3 , for determining one or more characteristics of a biological sample.

As another example, in some embodiments, server(s) 1110 may access information stored in database(s) 1106 and/or 1116 and use this information to perform process 400, described with reference to FIG. 4 , for determining tissue of origin for some or all cells in a biological sample.

As another example, in some embodiments, server(s) 1110 may access information stored in database(s) 1106 and/or 1116 and use this information to perform process 500, described with reference to FIG. 5 , for determining cancer grade for some or all cells in a biological sample.

As another example, in some embodiments, server(s) 1110 may access information stored in database(s) 1106 and/or 1116 and use this information to perform process 800, described with reference to FIG. 8 , for selecting a gene set.

As another example, in some embodiments, server(s) 1110 may access information stored in database(s) 1106 and/or 1116 and use this information to perform process 2900, described with reference to FIG. 29 , for determining PTCL subtype of a biological sample. In some embodiments, server(s) 1110 may include one or multiple computing devices. When server(s) 1110 include multiple computing devices, the device(s) may be physically co-located (e.g., in a single room) or distributed across multi-physical locations. In some embodiments, server(s) 1110 may be part of a cloud computing infrastructure. In some embodiments, one or more server(s) 1110 may be co-located in a facility operated by an entity (e.g., a hospital, research institution) with which doctor 1114 is affiliated. In such embodiments, it may be easier to allow server(s) 1110 to access private medical data for the patient 1102.

As shown in FIG. 11 , in some embodiments, the results of the analysis performed by server(s) 1110 may be provided to doctor 1114 through a computing device 1112 (which may be a portable computing device, such as a laptop or smartphone, or a fixed computing device such as a desktop computer). The results may be provided in a written report, an e-mail, a graphical user interface, and/or any other suitable way. It should be appreciated that although in the embodiment of FIG. 11 , the results are provided to a doctor, in other embodiments, the results of the analysis may be provided to patient 1102 or a caretaker of patient 1102, a healthcare provider such as a nurse, or a person involved with a clinical trial.

In some embodiments, the results may be part of a graphical user interface (GUI) presented to the doctor 1114 via the computing device 1112. In some embodiments, the GUI may be presented to the user as part of a webpage displayed by a web browser executing on the computing device 1112. In some embodiments, the GUI may be presented to the user using an application program (different from a web-browser) executing on the computing device 1112. For example, in some embodiments, the computing device 1112 may be a mobile device (e.g., a smartphone) and the GUI may be presented to the user via an application program (e.g., “an app”) executing on the mobile device.

The GUI presented on computing device 1112 may provide a wide range of oncological data relating to both the patient and the patient's cancer in a new way that is compact and highly informative. Previously, oncological data was obtained from multiple sources of data and at multiple times making the process of obtaining such information costly from both a time and financial perspective. Using the techniques and graphical user interfaces illustrated herein, a user can access the same amount of information at once with less demand on the user and with less demand on the computing resources needed to provide such information. Low demand on the user serves to reduce clinician errors associated with searching various sources of information. Low demand on the computing resources serves to reduce processor power, network bandwidth, and memory needed to provide a wide range of oncological data, which is an improvement in computing technology. All definitions, as defined and used herein, should be understood to control over dictionary definitions, and/or ordinary meanings of the defined terms.

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Such terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term).

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing”, “involving”, and variations thereof, is meant to encompass the items listed thereafter and additional items.

Having described several embodiments of the techniques described herein in detail, various modifications, and improvements will readily occur to those skilled in the art. Such modifications and improvements are intended to be within the spirit and scope of the disclosure. Accordingly, the foregoing description is by way of example only, and is not intended as limiting. The techniques are limited only as defined by the following claims and the equivalents thereto. 

What is claimed is: 1-30. (canceled)
 31. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions configured to perform a method comprising: accessing a trained statistical model comprising parameters estimated using training data, the training data indicating a plurality of gene rankings of at least some genes in a set of genes, the parameters being estimated after the at least some genes in the set of genes were selected; obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes; ranking the at least some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic.
 32. The system of claim 31, wherein the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample.
 33. The system of claim 31, wherein the at least one characteristic includes cancer grade for cells in the biological sample, and the cancer grade is selected from the group consisting of Grade 1, Grade 2, Grade 3, Grade 4, and Grade
 5. 34. The system of claim 31, wherein the at least one characteristic includes tissue of origin for cells in the biological sample, and the tissue of origin is selected from the group consisting of lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue.
 35. The system of claim 31, wherein the at least one characteristic includes tissue type for cells in the biological sample, and the tissue type is selected from the group consisting of adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma.
 36. The system of claim 31, wherein the at least one characteristic includes human papillomavirus (HPV) status for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table
 8. 37. The system of claim 31, wherein the at least one characteristic includes a subtype of peripheral T-cell lymphoma (PTCL) for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table
 10. 38. The system of claim 37, wherein the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL).
 39. The system of claim 31, wherein the method further comprises: presenting, to a user, an indication of the at least one characteristic via a graphical user interface (GUI).
 40. A method, comprising: using at least one computer hardware processor to perform: accessing a trained statistical model comprising parameters estimated using training data, the training data indicating a plurality of gene rankings of some genes in a set of genes, the parameters being estimated after the some genes in the set of genes were selected; obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes; ranking the some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic.
 41. The method of claim 40, wherein the at least one characteristic includes cancer grade for cells in the biological sample.
 42. The method of claim 41, wherein the subject has, is suspected of having, or is at risk of having breast cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table
 1. 43. The method of claim 42, wherein the set of genes comprises at least 10 genes selected from the group of genes listed in Table
 1. 44. The method of claim 41, wherein the subject has, is suspected of having, or is at risk of having kidney cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table
 2. 45. The method of claim 41, wherein the subject has, is suspected of having, or is at risk of having lymphoma, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table
 3. 46. The method of claim 45, wherein the subject has, is suspected of having, or is at risk of having Diffuse Large B-Cell Lymphoma (DLBCL), the set of genes comprises at least 10 genes selected from the group of genes listed in Table 3, and the at least one characteristic is a cell of origin selected from the group consisting of germinal center B-cell (GCB) and activated B-cell (ABC).
 47. The method of claim 40, wherein the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample.
 48. At least one non-transitory computer-readable storage medium storing processor-executable instructions that are configured to cause, when executed by at least one computer hardware processor, the at least one computer hardware processor to perform: accessing a trained statistical model previously trained and comprising parameters estimated using training data after at least some genes in a set of genes were selected, the training data indicating a plurality of gene rankings of the at least some genes in the set of genes; obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes; ranking the at least some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic.
 49. The at least one non-transitory computer-readable storage medium of claim 48, wherein the gene ranking includes a value identifying a relative rank for each gene of the at least some genes in the set of genes.
 50. The at least one non-transitory computer-readable storage medium of claim 48, wherein the gene ranking includes values identifying relative ranks of the at least some genes in the gene ranking, wherein the values are different from the first expression levels. 